Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by (...) using different machine learning algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

This study explored the global cyberspace security issues, with the purpose of breaking the stereotype of people’s cognition of cyberspace problems, which reflects the relationship between interdependence and association. Based on the Apriori algorithm in association rules, a total of 181 strong rules were mined from 40 target websites and 56,096 web pages were associated with global cyberspace security. Moreover, this study analyzed support, confidence, promotion, leverage, and reliability to achieve comprehensive coverage of data. A total of (...) 15,661 sites mentioned cyberspace security-related words from the total sample of 22,493 professional websites, accounting for 69.6%, while only 735 sites mentioned cyberspace security-related words from the total sample of 33,603 non-professional sites, accounting for 2%. Due to restrictions of language, the number of samples of target professional websites and non-target websites is limited. Meanwhile, the number of selections of strong rules is not satisfactory. Nowadays, the cores of global cyberspace security issues include internet sovereignty, cyberspace security, cyber attack, cyber crime, data leakage, and data protection. (shrink)

In recent years, the network has become more complex, and the attacker’s ability to attack is gradually increasing. How to properly understand the network security situation and improve network security has become a very important issue. In order to study the method of extracting information about the security situation of the network based on cloud computing, we recommend the technology of knowledge of the network security situation based on the data extraction technology. It converts each (...) received cyber security event into a standard format that can be defined as multiple brochures, creating a general framework for the cyber security situation. According to the large nature of network security situation data, the Hadoop platform is used to extract aggregation rules, and perform model extraction, pattern analysis, and learning on a network security event dataset to complete network security situation rule mining, and establish a framework for assessing the state of network security. According to the results of the federal rule extraction, the level of network node security risk is obtained in combination with signal reliability, signal severity, resource impact, node protection level, and signal recovery factor. A simulation test is performed to obtain the intrusion index according to the source address of the network security alarm. Through the relevant experiments and analysis of the results, the attack characteristics obtained in this study were obtained after manually reducing the network security event in the 295 h window. The results show that after the security event is canceled, the corresponding window attack index decreases to 0, indicating that this method can effectively implement a network security situation awareness. The proposed technique allows you to accurately sense changes in network security conditions. (shrink)

Due to the complexity and versatility of network security alarm data, a cloud-based network security data extraction method is proposed to address the inability to effectively understand the network security situation. The information properties of the situation are generated by creating a set of spatial characteristics classification of network security knowledge, which is then used to analyze and optimize the processing of hybrid network security situation information using cloud computing technology and co-filtering technology. (...) Knowledge and information about the security situation of a hybrid network has been analyzed using cloud computing strategy. The simulation results show that a cyber security crash occurs in window 20, after which the protection index drops to window 500. The increase in the security index of 500 windows is consistent with the effectiveness of the concept of this document method, indicating that this document method can sense changes in the network security situation. Starting from the first attacked window, the defense index began to decrease. In order to simulate the added network defense, the network security events in the 295th time window were reduced in the original data, and the defense index increased significantly in the corresponding time period, which is consistent with the method perception results, which further verifies the effectiveness and reliability of this method on the network security event perception. This method provides high-precision knowledge of network security situations and improves the security and stability of cloud-based networks. (shrink)

Recently, there has been a heavy debate in the US about the government’s use of data mining in its fight against terrorism. Privacy concerns in fact led the Congress to terminate the funding of TIA, a program for advanced information technology to be used in the combat of terrorism. The arguments put forward in this debate, more specifically those found in the main report and minority report by the TAPAC established by the Secretary of Defense to examine the (...) TIA issue, will be analysed to trace the deeper roots of this controversy. This analysis will in turn be used as a test case to examine the adequacy of the usual theoretical frameworks for these kinds of issues, in particular the notion of privacy. Whereas the dominant theoretical framing of the notion of privacy turns around access to information, most of the core arguments in the debate do not fit in this kind of framework. The basic disagreements in the controversy are not about mere access, they involve both access and use. Furthermore, whereas the issue of access by itself refers to a more or less static situation, the real disagreements much more concern the organisational dynamics of the use of information, the mechanisms in the organisation that control these dynamics, and the awareness present within the organisation of the ‘social risks’ these dynamics represent. The bottom line question is whether the assessment of these gives sufficient reason for trust. (shrink)

The power grid is an important connection between power sources and users, responsible for supplying and distributing electric energy to users. Modern power grids are widely distributed and large in scale, and their security faces new problems and challenges. Information entropy theory is an objective weighting method that compares the information order of each evaluation index to judge the weight value. With the wide application of entropy theory in various disciplines, the subject of introducing entropy into the power system (...) has been gradually concerned. This article aims to study the smart terminal security technology of the power grid perception layer based on information entropy data mining. This article analyzes its related methods and designs a smart terminal for the power grid. On this basis, a data analysis platform is built and a safety plan is designed. The result is that the average absolute error, root mean square error, average absolute percentage error, and mean square error of the platform's power load forecast are 1.58, 1.96, 8.2%, and 3.93, respectively. These error values are within the ideal range, and the data processing ability is strong. The packet loss rate of the adversary's eavesdropping was tested, and the average packet loss rates at locations a, b, c, and d were 1.05, 1.2, 1.81, and 2.2%, respectively. Data packets will be definitely lost, so the platform is highly secure. (shrink)

Guided by the theories of system theory, synergetic theory, and other disciplines and based on fuzzy data mining algorithm, this article constructs a three-tier social security fund cloud audit platform. Firstly, the article systematically expounds the current situation of social security fund and social security fund audit, such as the technical basis of cloud computing and data mining. Combined with the actual work, the necessity and feasibility of building a cloud audit platform for (...) social security funds are analyzed. This article focuses on the construction of the cloud audit platform for social security funds. The general idea of using fuzzy data mining algorithm to build the social security fund audit cloud platform is to compress the knowledge contained in a large number of data into the weights between nodes and optimize the weights through the learning of the neural network system. Through the optimization function, the information contained in the neural network is stored in a few weights as far as possible. The main information is further highlighted by network clipping and removing weights that have little impact on the output. (shrink)

This chapter addresses the morality of two types of national security electronic surveillance (SIGINT) programs: the analysis of communication “metadata” and dragnet searches for keywords in electronic communication. The chapter develops a standard for assessing coercive government action based on respect for the autonomy of inhabitants of liberal states and argues that both types of SIGINT can potentially meet this standard. That said, the collection of metadata creates opportunities for abuse of power, and so judgments about the trustworthiness and (...) competence of the government engaging in the collection must be made in order to decide whether metadata collection is justified in a particular state. Further, the moral standard proposed has a reflexive element justifying adversary states’ intelligence collection against one another. Therefore, high-tech forms of SIGINT can only be justified at the cost of justifying cruder versions of signals intelligence collection practiced by a state’s less-advanced adversaries. (shrink)

One of the crucial fields of study that is getting more attention is association rule mining. It is crucial to databases' knowledge discovery (KDD). KDD and association rule mining have a very broad use. It has evolved at a rapid rate over the past fifteen years. Association Rule Mining is a novel technology, although it is still in the discovery and development stages. Video is an illustration of interactive media data since it contains text, pictures, meta- (...) class='Hi'>data, visual, sound, and different kinds of data. It is frequently used for a variety of important likely reasons, such as security and reconnaissance, amusement, healthcare, educational endeavours, and sports. One of the main issues for the local data-mining research community is video data mining, which aims to find and comprehend fascinating examples from the large volume of video data. Video data mining is still in its infancy when compared to mining other sorts of data. It is pivotal to grasp and naturally mine the information from video databases considering the continually extending computerized libraries and video databases. Differentiating association rules between components in a significant video library is critical in the field of video data mining. As per continuous imaginative work, association rule mining is being utilized rapidly in various tasks, including perception, social affairs, broadcast news, sports, records, films, clinical data, as well as on the web and individual media assortments. This exploration tries to give a far-reaching method to association rule extraction from video databases. (shrink)

In order to correct the deficiencies of intrusion detection technology, the entire computer and network security system are needed to be more perfect. This work proposes an improved k-means algorithm and an improved Apriori algorithm applied in data mining technology to detect network intrusion and security maintenance. The classical KDDCUP99 dataset has been utilized in this work for performing the experimentation with the improved algorithms. The algorithm’s detection rate and false alarm rate are compared with the (...) experimental data before the improvement. The outcomes of proposed algorithms are analyzed in terms of various simulation parameters like average time, false alarm rate, absolute error as well as accuracy value. The results show that the improved algorithm advances the detection efficiency and accuracy using the designed detection model. The improved and tested detection model is then applied to a new intrusion detection system. After intrusion detection experiments, the experimental results show that the proposed system improves detection accuracy and reduces the false alarm rate. A significant improvement of 90.57% can be seen in detecting new attack type intrusion detection using the proposed algorithm. (shrink)

This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the (...) decision forest data mining algorithm to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves. (shrink)

Motivation: • Breaking barriers in publishing demands a proactive attitude • Open data, open review and open dialogue in making social sciences plausible .

This essay examines issues involving personal privacy and informed consent that arise at the intersection of information and communication technology and population genomics research. I begin by briefly examining the ethical, legal, and social implications program requirements that were established to guide researchers working on the Human Genome Project. Next I consider a case illustration involving deCODE Genetics, a privately owned genetics company in Iceland, which raises some ethical concerns that are not clearly addressed in the current ELSI guidelines. The (...) deCODE case also illustrates some ways in which an ICT technique known as data mining has both aided and posed special challenges for researchers working in the field of population genomics. On the one hand, data-mining tools have greatly assisted researchers in mapping the human genome and in identifying certain “disease genes” common in specific populations. On the other hand, this technology has significantly threatened the privacy of research subjects participating in population genomics studies, who may, unwittingly, contribute to the construction of new groups that put those subjects at risk for discrimination and stigmatization. In the final section of this paper I examine some ways in which the use of data mining in the context of population genomics research poses a critical challenge for the principle of informed consent, which traditionally has played a central role in protecting the privacy interests of research subjects participating in epidemiological studies. (shrink)

The present study examines certain challenges that KDD (Knowledge Discovery in Databases) in general and data mining in particular pose for normative privacy and public policy. In an earlier work (see Tavani, 1999), I argued that certain applications of data-mining technology involving the manipulation of personal data raise special privacy concerns. Whereas the main purpose of the earlier essay was to show what those specific privacy concerns are and to describe how exactly those concerns have (...) been introduced by the use of certain KDD and data-mining techniques, the present study questions whether the use of those techniques necessarily violates the privacy of individuals. This question is considered vis-à-vis a recent theory of privacy advanced by James Moor (1997). The implications of that privacy theory for a data-mining policy are also considered. (shrink)

Student performance prediction aims to evaluate the grade that a student will reach before enrolling in a course or taking an exam. This prediction problem is a kernel task toward personalized education and has attracted increasing attention in the field of artificial intelligence and educational data mining. This paper provides a systematic review of the SPP study from the perspective of machine learning and data mining. This review partitions SPP into five stages, i.e., data collection, (...) problem formalization, model, prediction, and application. To have an intuition on these involved methods, we conducted experiments on a data set from our institute and a public data set. Our educational dataset composed of 1,325 students, and 832 courses was collected from the information system, which represents a typical higher education in China. With the experimental results, discussions on current shortcomings and interesting future works are finally summarized from data collections to practices. This work provides developments and challenges in the study task of SPP and facilitates the progress of personalized education. (shrink)

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue (...) that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience. (shrink)

Objective To compare assessments of the decision-making competencies of psychiatric inpatients as provided by physicians, nurses, relatives and an assessment tool.Methods This study was carried out at the psychiatry clinic of Kocaeli University Hospital from June 2007 to February 2008. The decision-making competence of the 83 patients who participated in the study was assessed by physicians, nurses, relatives and MacCAT-T.Results Of the 83 patients, the relatives of 73.8% of them, including the parents of 47.7%, were interviewed during the study. A (...) moderately good consistency between the competency assessments of the nurses versus those of the physicians, but a poor consistency between the assessments of the physicians and nurses versus those of the patients’ relatives, was determined. The differences in the competency assessment obtained with the MacCAT-T versus the evaluations of the physicians, nurses and patients’ relatives were statistically significant.Conclusions Our findings demonstrate those physicians, nurses and the patients’ relatives have difficulty in identifying patients lacking decision-making competence. Therefore, an objective competence assessment tool should be used along with the assessments of physicians and nurses, both of whom can provide clinical data, as well as those of relatives, who can offer insights into the patient's moral values and expectations. (shrink)

This case study introduces the QIT Madagascar Minerals (QMM) SA mining project at Fort-Dauphin, Madagascar, as a development project that has produced issues concerning justice. Although QMM appears to be a model company with a project that is seen as a success story, its consequent displacement of populations has been problematic in many respects, as have been the social effects that arise due to migration to the area by others who are attracted by the project. We suggest that the (...) root of many of these problems is that the people to be displaced have played an insignificant role in forming the project itself: consultation has instead been centred on corporate, government and environmental NGO concerns and interests. Compensation for losses has been inadequate. As non-mining land has been set aside as an offset for the purpose of conservation, traditional livelihoods have been curtailed. A narrow and Western conception of natural conservation that forbids access by the people has ensued for lands that hav.. (shrink)

In recent years, with the development of construction industry, more scientific, systematic, fast, and intelligent calculation methods are needed to coordinate urban development and fierce market competition, and mathematical algorithm library plays an important role in artificial intelligence. Therefore, the author uses computer mathematical algorithm and extension theory to study and analyze the residential building design and intelligent data mining. It is found that the research of the computer-aided expression method of extension building planning is mainly the expression (...) of the input and output system of extension building planning. It includes knowledge representation, system outline design, system flow, and interface expression based on the mathematical database. (shrink)

Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka tool, (...) is the analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey. (shrink)

:This article examines privacy threats arising from the use of data mining by private Australian health insurance companies. Qualitative interviews were conducted with key experts, and Australian governmental and nongovernmental websites relevant to private health insurance were searched. Using Rationale, a critical thinking tool, the themes and considerations elicited through this empirical approach were developed into an argument about the use of data mining by private health insurance companies. The argument is followed by an ethical analysis (...) guided by classical philosophical theories—utilitarianism, Mill’s harm principle, Kant’s deontological theory, and Helen Nissenbaum’s contextual integrity framework. Both the argument and the ethical analysis find the use of data mining by private health insurance companies in Australia to be unethical. Although private health insurance companies in Australia cannot use data mining for risk rating to cherry-pick customers and cannot use customers’ personal information for unintended purposes, this article nonetheless concludes that the secondary use of customers’ personal information and the absence of customers’ consent still suggest that the use of data mining by private health insurance companies is wrong. (shrink)

Earth-shaping catastrophic events have long focused the attention of the geographical and geological sciences, and captured the public imagination. During the past 40 years, neocatastrophism has emerged as a key paradigm that reflects widespread changes involving cultural, scientific, political and technological spheres. Nonetheless, the extent, chronology and origin of this trend are equivocal. Here, we use Google Ngram to quantitatively explore the recent development of catastrophism. We elucidate a discernable rise in neocatastrophic thinking during the last quarter of the twenty-first (...) century that can be linked to the environmental awakening of the 1960s. It is suggested that these discourses of ‘shock’ and ‘fear’ partly correspond to a media-driven dramatization of natural hazards, exploited by scientists and journalists to attract wider readership. (shrink)

Pharmaceutical companies have long relied on direct marketing of their drugs to physicians through one-on-one meetings with sales representatives. This practice of “detailing” is substantial in its costs and its number of participants. Every year, pharmaceutical companies spend billions of dollars on millions of visits to physicians by tens of thousands of sales representatives.Critics have argued that drug detailing results in sub-optimal prescribing decisions by physicians, compromising patient health and driving up spending on medical care. In this view, physicians often (...) are unduly influenced both by marketing presentations that do not accurately reflect evidence from the medical literature and by the gifts that sales representatives deliver in conjunction with their presentations. (shrink)

With the development of the digital economy, the emerging marketing strategy of the e-commerce flash sales has been changing the traditional purchasing habits of customers. This imposes new decision-making challenges for companies involved in flash sales. It is important for companies to build the accurate product demand forecast analysis focusing on the characteristics of the flash sales and customer behaviors. In this paper, VIPS is taken as a case study with the key focus on how sentiment factors in customer reviews (...) affect product demand in flash sale platforms. The paper adopts two sentiment analysis methods based on emotional dictionaries. The method with a higher evaluation index is adopted to integrate the emotional factors into the autoregressive model for product demand and assessment. The experiments prove that the autoregressive model for integrating the sentiment factors demonstrates better forecasting performances than the models without sentiment factors. The experiments further confirm that when product demand for the previous two weeks and customer review sentiment factors in the previous week are taken into consideration, demand forecast effects are most accurate. (shrink)

Privacy concerns involving data mining are examined in terms of four questions: What exactly is data mining? How does data mining raise concerns for personal privacy? How do privacy concerns raised by data mining differ from those concerns introduced by traditional information-retrieval techniques in computer databases? How do privacy concerns raised by mining personal data from the Internet differ from those concerns introduced by mining such data from (...) class='Hi'>data warehouses? It is argued that the practice of using data-mining techniques, whether on the Internet or in data warehouses, to gain information about persons raises privacy concerns that go beyond concerns introduced in traditional information-retrieval techniques in computer databases and are not covered by present data-protection guidelines and privacy laws. (shrink)

Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for (...) the purposes of making computers understand medical natural language. (shrink)

Historic preservation, which is a vital act for conveying people’s understanding of the past, such as events, ideas, and places to the future, allows people to preserve history for future generations. Additionally, since the historic properties are currently concentrated in urban areas, an urban-oriented approach will contribute to the issue. Hence, public awareness is a key factor that paves the way for this conservation. Public history, a history with a public audience and special methods of representation, can serve society in (...) this regard. Historical video games, which get their game objectives from the past, can be considered a form of public history, raising people’s awareness and letting them gain historical experience. While plenty of studies focused on this content with various approaches, this study takes the topic under consideration with a player-oriented focus. The research uses the Steam video game digital distribution service database to explore the existing historical games based on their genre, player scale, dimension, VR compatibility attributes, and player rating. The database includes 1,148 games published between 2002 and 2022, filtered as historical by Steam tags and sorted by player review. For the data collection, the study uses the Octoparse web scraping tool for data extraction, and for the data analysis, it uses RapidMiner Studio data mining software. The study aims to provide a framework applicable to the development of commercially playable historical video games contributing to heritage preservation by raising public awareness in the context of public history. (shrink)

In recent years, learning materials have gradually been applied to flipped classrooms. Teachers share learning materials, and students can preview the learning materials before class. During class, the teacher can discuss students' questions from their notes from previewing the learning materials. The social media platform Facebook provides access to learning materials and diversified interactions, such as sharing knowledge, annotating learning materials, and establishing common objectives. Previous studies have explored the effect of flipped classrooms on students' learning engagement, attitudes, and performance. (...) In this paper, we apply educational data mining to explore the relationship between students' viewing behaviors in accessing learning materials and their performance in flipped classrooms. The participants are classified into an experimental group and a control group to engage in flipped classroom activities. The experimental group uses the social media platform Facebook for flipped learning, and the control group uses a learning management system for flipped learning. The results show that there is a significant difference in the learning performance between the two groups, with the average score of the experimental group being higher than that of the control group. Furthermore, we find that the viewing behaviors and performance of the students within the experimental group differ significantly. (shrink)

In light of Taiwan's extensive exposure to the Chinese Communist Party's "cognitive domain infiltration warfare," this paper proposes new response mechanisms and strategies for cybersecurity and national defense. The focus is primarily on assessing the CCP's cognitive infiltration tactics to develop policy recommendations in cybersecurity linguistics. These recommendations are intended to serve as a reference for future national defense and information security policies. Within the constraints of limited resources, this study attempts to provide an integrated analysis method combining qualitative (...) and quantitative tools. This method involves text mining in cybersecurity linguistics, focusing on "textual content" and incorporating discourse deconstruction. The aim is to enhance the success rate in identifying and defining contentious information in cognitive warfare, thereby offering policy suggestions. -/- Keywords: Cognitive Infiltration, Big Data Corpus, Cybersecurity Linguistics, Text Mining Analysis, Narrative Language Deconstruction. (shrink)

The paper discusses the problems in the detection of security information in legal and other administrative data. The authors analyse the prospects of the use of datamining in the solution of two key problems: abundance and indirectness of these data. Security research uses two kinds of data. The first one is scientific data, designed and gathered specially for the verification of certain security theories. They are the data of criminological, sociological, psychological surveys, (...) experimental data, etc. The second kind is data that are not designed for security research. Most of the data are the by-products of the national legal system, especially its criminal justice, of national and local institutions responsible for the maintenance of public order, and of other public agencies. All the data provide huge amounts of information destined to control and direct the activities of these institutions. There are two kinds of problems in the use of this information in security study. First, this information has to be ‘decoded’ from data describing the activities of related institutions. Second, data on security are lost in huge amounts of other public data. Thus, they have to be ‘mined’. The paper discusses the prospects of the modern information proceeding method—data-mining in the solution of both problems. A new concept of data mining as a meta-procedure is proposed. The object of this meta-procedure is supposed to be the integration of multiple current theories (for example, criminal security) and related statistical procedures. A general algorithm of such data-mining is proposed. (shrink)

The IVF ET method is a scientifically recognized infertility treat- ment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treat- ment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining gives (...) hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process. (shrink)

The study of school effectiveness and the identification of factors associated with it are growing fields of research in the education sciences. Moreover, from the perspective of data mining, great progress has been made in the development of algorithms for the modeling and identification of non-trivial information from massive databases. This work, which falls within this context, proposes an innovative approach for the identification and characterization of educational and organizational factors associated with high school effectiveness. Under a perspective (...) of basic research, our aim is to study the suitability of decision trees, techniques inherent to data mining, to establish predictive models for school effectiveness. Based on the available Spanish sample of the PISA 2015 assessment, an indicator of the school effectiveness was obtained from the application of multilevel models with predictor variables of a contextual nature. After selecting high- and low-effectiveness schools in this first phase, the second phase of the study was carried out and consisted of the application of decision trees to identify school, teacher, and student factors associated with high and low effectiveness. The C4.5 algorithm was calculated and, as a result, we obtained 120 different decision trees based on five determining factors (database used; stratification in the initial selection of schools; significance of the predictor variables of the models; use of items and/or scales; and use of the training or validated samples). The results show that the use of this kind of technique could be appropriate if mainly used with correctly pre-processed data that include the combined information available from all educational agents. This study represents a major breakthrough in the study of the factors associated with school effectiveness from a quantitative approach, since it proposes and provides a simple and appropriate procedure for modeling and establishing patterns. In doing so, it contributes to the development of knowledge in the field of school effectiveness that can help in educational decision-making. (shrink)

Traditional libraries will evolve to digital libraries which are clearly superior at: Dissemination, sharing, linking, storing, and information variety. Therefore, one can say that electronic libraries have specific needs in terms of content, services and long-term preservation. In contrast, digital libraries suffer from several inherent constraints: storage limitation, performance, relevancy, decentralization, lack of semantic, fault tolerance, scalability. The main intention of this paper is to present a design of an integrated digital library system based on peer-to-peer data mining. (...) This article aims also to prove that peer-to-peer mining, an emerging branch of distributed data mining, is a hot research area well suited to overcome intrinsic problems of digital libraries. (shrink)

Because obituaries are succinct and explicitly intended to summarize their subjects’ lives, they may be expected to include only the features that the author finds most salient but also to signal to others in the community the socially-recognized aspects of the deceased’s character. We begin by reviewing studies 1 and 2, in which obituaries were carefully read and labeled. We then report study 3, which further develops these results with a semi-automated, large-scale semantic analysis of several thousand obituaries. Geography, gender, (...) and elite status all turn out to be associated with the virtues and values associated with the deceased. (shrink)

This article develops a more comprehensive understanding of data mining by examining the application of this technology in the marketplace. In addition to exploring the technological issues that arise from the use of these applications, we address some of the social concerns that are too often ignored.As more firms shift more of their business activities to the Web, increasingly more information about consumers and potential customers is being captured in Web server logs. Sophisticated analytic and data (...) class='Hi'>mining software tools enable firms to use the data contained in these logs to develop and implement a complex relationship management strategy. Although this new trend in marketing strategy is based on the old idea of relating to customers as individuals, customer relationship management actually rests on segmenting consumers into groups based on profiles developed through a firm's data mining activities. Individuals whose profiles suggest that they are likely to provide a high lifetime value to the firm are served content that will vary from that which is served to consumers with less attractive profiles. (shrink)

Health‐related data uses and data sharing have been in the spotlight for a while. Since the beginning of the big data era, massive data mining and its inherent possibilities have only increased the debate about what the limits are. Data governance is a relevant aspect addressed in ethics guidelines. In this context, the European project BRIDGE Health (BRidging Information and Data Generation for Evidence‐based Health policy and research) strove to achieve a comprehensive, integrated (...) and sustainable EU health‐information system. One of the aims of the project was to evaluate the requirements to construct a data‐linkage infrastructure for the secure management of health information. In a blueprint provided for this infrastructure, the topics ethics and the intimately related governance occupied a whole section, where the recent ethics guidelines by the Council for International Organizations of Medical Sciences (CIOMS) and the World Medical Association (WMA) were referenced. We explore what has changed in the latest versions of the ethics documents adopted by CIOMS and WMA regarding the management of health data and human tissues, the appropriateness of their application in new forms of research and infrastructures as the proposed in the BRIDGE Health project, and whether society should be so concerned about this topic, in the digital era of social exchange. (shrink)

The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors’ work with three Police Services. The focus is upon the use of “soft” forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than “hard” (...) evidence such as DNA or fingerprint evidence. Three objectives underpin this paper. First, given the continuing expansion of forensic computing and its role in the emergent discipline of Crime Science, it is timely to present a review of existing methodologies and research. Second, it is important to extract some practical lessons concerning the application of computer science within this forensic domain. Finally, from the lessons to date, a set of conclusions will be advanced, including the need for multidisciplinary input to guide further developments in the design of such systems. The objectives are achieved by first considering the task performed by the intended systems users. The discussion proceeds by identifying the portions of these tasks for which automation would be both beneficial and feasible. The knowledge discovery from databases process is then described, starting with an examination of the data that police collect and the reasons for storing it. The discussion progresses to the development of crime matching and predictive knowledge which are operationalised in decision support software. The paper concludes by arguing that computer science technologies which can support criminal investigations are wide ranging and include geographical information systems displays, clustering and link analysis algorithms and the more complex use of data mining technology for profiling crimes or offenders and matching and predicting crimes. We also argue that knowledge from disciplines such as forensic psychology, criminology and statistics are essential to the efficient design of operationally valid systems. (shrink)

New methods to analyse social media data provide a powerful way to know publics and capture what they say and do. At the same time, access to these methods is uneven, with corporations and governments tending to have best access to relevant data and analytics tools. Critics raise a number of concerns about the implications dominant uses of data mining and analytics may have for the public: they result in less privacy, more surveillance and social discrimination, (...) and they provide new ways of controlling how publics come to be represented and so understood. In this paper, we consider if a different relationship between the public and data mining might be established, one in which publics might be said to have greater agency and reflexivity vis-à-vis data power. Drawing on growing calls for alternative data regimes and practices, we argue that to enable this different relationship, data mining and analytics need to be democratised in three ways: they should be subject to greater public supervision and regulation, available and accessible to all, and used to create not simply known but reflexive, active and knowing publics. We therefore imagine conditions in which data mining is not just used as a way to know publics, but can become a means for publics to know themselves. (shrink)

This article responds to two problems confronting social and human sciences: how to relate to digital data, inasmuch as it challenges established social science methods; and how to relate to life sciences, insofar as they produce knowledge that impinges on our own ways of knowing. In a case study of proteomics, we explore how digital devices grapple with large-scale multiples – of molecules, databases, machines and people. We analyse one particular visual device, a cluster-heatmap, produced by scientists by (...) class='Hi'>mining data from a large number of experiments on human blood plasma proteins. These proteins make up a myriad multiple whose identity shifts in many ways. Rather than displaying data about proteins, the heatmap constructs a view of the differences and similarities between experiments. We find this attempt to construct a view on many things at once instructive in thinking about multiples more generally. Instead of flattening molecular ‘life itself’, this visual device superimposes layers of digital devices and techniques from a wide variety of disciplines. This layering suggests a different way of relating to the life sciences more generally: rather than what they know, how they know might be of use to social and human sciences when attending to multiplicities. (shrink)

This paper copes with a real-world classification problem related to the management of claims received in an insurance company. The way to obtain the classifier is not easy due to the high amount of missing values as well as the inherent imbalanced scenario within class labels. Once the data partition has been done, the training set is submitted to an intensive double grid search in order to obtain the most promising type of missing value imputation approach and then a (...) step ahead is done using the best method and it starts the next round of data mining strategies which now falls into data rebalancing umbrella. Again, a grid search from an undersampling and oversampling family with different settings is done taking into account only seen data. The training data obtained after the first grid search are now submitted to the second step according the second grid search in order to get the ready training set for the further classifier training. The main objective of the work is to find the best combination of data mining techniques that suits the data set with a pipeline containing two types of data preparation methods coming from different families. As an outcome, first the problem of the presence of missing values has been addressed and then the data rebalancing techniques has been applied. The study focuses on obtaining classifiers based on Bayesian and lazy approaches as well as decision trees, evaluated on metrics such as the area under the ROC curve (AUC), Cohen’s kappa, Accuracy and the F-measure, among others. The imputation by the mean the mode is preferable to the Expectation Maximization Imputation in the scenario faced in this paper taking into account that the amount of missing values is higher than a forty percent for many features. (shrink)

Decision makers in banking, insurance or employment mitigate many of their risks by telling “good” individuals and “bad” individuals apart. Laws codify societal understandings of which factors are legitimate grounds for differential treatment —or are considered unfair discrimination, including gender, ethnicity or age. Discrimination-aware data mining implements the hope that information technology supporting the decision process can also keep it free from unjust grounds. However, constraining data mining to exclude a fixed enumeration of potentially discriminatory features (...) is insufficient. We argue for complementing it with exploratory DADM, where discriminatory patterns are discovered and flagged rather than suppressed. This article discusses the relative merits of constraint-oriented and exploratory DADM from a conceptual viewpoint. In addition, we consider the case of loan applications to empirically assess the fitness of both discrimination-aware data mining approaches for two of their typical usage scenarios: prevention and detection. Using Mechanical Turk, 215 US-based participants were randomly placed in the roles of a bank clerk or a citizen / policy advisor. They were tasked to recommend or predict the approval or denial of a loan, across three experimental conditions: discrimination-unaware data mining, exploratory, and constraint-oriented DADM. The discrimination-aware tool support in the eDADM and cDADM treatments led to significantly higher proportions of correct decisions, which were also motivated more accurately. There is significant evidence that the relative advantage of discrimination-aware techniques depends on their intended usage. For users focussed on making and motivating their decisions in non-discriminatory ways, cDADM resulted in more accurate and less discriminatory results than eDADM. For users focussed on monitoring for preventing discriminatory decisions and motivating these conclusions, eDADM yielded more accurate results than cDADM. (shrink)

Data mining is on the interface of Computer Science and Statistics, utilizing advances in both disciplines to make progress in extracting information from large databases. It is an emerging field that has attracted much attention in a very short period of time. This article highlights some statistical themes and lessons that are directly relevant to data mining and attempts to identify opportunities where close cooperation between the statistical and computational communities might reasonably provide synergy for further (...) progress in data analysis. (shrink)