This article focuses on the issue of data mining as it relates to the consumer and to the issue of whether the consumer's private information has any proprietary status. A brief review of data mining is provided as a background for a better understanding of the purposes and uses of data mining. Also examined are several issues of the ethics of data mining, including a review of stakeholders, who they are and which (...) may be most seriously affected by unethical data mining practices. Several suggestions for the improvement of data mining as it relates to the consumer are further presented: suggestions that would allow for data mining that would be beneficial to both the business community and the consumer. (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)

• Conventional data mining infers relations among e.g. the fraction of supermarket baskets with diapers also contain beer. • Phenomenal data mining concerns relations between data and the phenomena underlying the data, e.g. y married couples keeping old friends buy diapers and • Example: The sales receipts of a supermarket usually not identify the customers. Grouping baskets by customer is possible and useful but requires new techniques.

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)

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

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 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)

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)

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)

High vocation education is an important foundation for China to cultivate high teaching quality and technical and skilled talents. In the new era, the acceleration of the development of modern vocational education is put in a more prominent position. It is proposed that we should adhere to moral education, closely combine this with the needs of technological change and industrial upgrading, constantly improve the quality of high vocational education teaching, and cultivate more technical and skilled talents with both political integrity (...) and ability for modernization construction. Under the background of social informatization, using artificial intelligent technology to solve these problems can play an important role for improving the teaching quality of high vocational education. This paper proposed a data mining approach in promoting student satisfaction with the teaching quality of high vocational education. We design a questionnaire for Students’ satisfaction with the teaching quality of basic entrepreneurship curriculum. We take the survey data of vocation education as an example and use mining technology analysis software to understand the current status of the teaching quality of basic entrepreneurship curriculum. The results determine the main factors affecting Students’ satisfaction with teaching quality. The results of this paper can be used in student management, education strategy, student education satisfaction, and teaching quality in high vocation college education, and to improve the teaching quality of fundamentals of entrepreneurship curriculum in high vocation education. (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)

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)

This paper presents the NeoCrawler – a tailor-made webcrawler, which identifies and retrieves neologisms from the Internet and systematically monitors the use of detected neologisms on the web by means of weekly searches. It enables researchers to use the web as a corpus in order to investigate the dynamics of lexical innovation on a large-scale and systematic basis. The NeoCrawler represents an innovative web-mining tool which opens up new opportunities for linguists to tackle a number of unresolved and under-researched (...) issues in the field of lexical innovation. This paper presents the design as well as the most important characteristics of two modules, the Discoverer and the Observer, with regard to the usage-based study of lexical innovation and diffusion. (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)

This essay traces the complex constellation of ideas that informs Anders's turn to the generalizing expression ‘the human’ in his postwar work. It mobilizes the properties of radioactive material and digital data, which are both curiously imperceptible to our senses, to discuss Anders’s insistence on the universalizing pronoun `we' and assess its significance in the contemporary world. To do so, it aligns Anders's work with current debates about the Anthropocene and critiques of the use of the term ‘the human’ (...) in postcolonial settings. (shrink)

Nowadays, the modern management is promoted to resolve the issue of unreliable information transmission and to provide work efficiency. The basic aim of the modern management is to be more effective in the role of the school to train talents and serve the society. This article focuses on the application of data mining (DM) in the development of information management system (IMS) in universities and colleges. DM provides powerful approaches for a variety of educational areas. Due to the (...) large amount of student information that can be used to design valuable patterns relevant to student learning behavior, research in the field of education is continuously expanding. Educational data mining can be used by educational institutions to assess student performance, assisting the institution in recognizing the student’s accomplishments. In DM, classification is a well-known technique that has been regularly used to determine student achievement. In this study, the process of DM and the application research of association rules is introduced in the development of IMS in universities and colleges. The results show that the curriculum covers the whole field and the minimum transaction support count be 2, minconf = 70%. The results also suggested that students who choose one course also tend to choose the other course. The application of DM theory in university information will greatly upsurge the data analysis capability of administrators and improve the management level. (shrink)

Data Mining es una tecnología de estadística computarizada para la extracción de pautas reiteradas y significativas a partir de datos cuantiosos. En este artículo se exponen primero los fundamentos para su aplicación al análisis de los textos. Después, se detalla la aplicación del análisis de afinidad a buena parte de los escritos de Tomás de Aquino. Y, finalmente, se muestran los resultados obtenidos del análisis de varios de sus comentarios paulinos.

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 (...) class='Hi'>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)

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)

Web mining refers to the whole of data miningand related techniques that are used toautomatically discover and extract informationfrom web documents and services. When used in abusiness context and applied to some type ofpersonal data, it helps companies to builddetailed customer profiles, and gain marketingintelligence. Web mining does, however, pose athreat to some important ethical values likeprivacy and individuality. Web mining makes itdifficult for an individual to autonomouslycontrol the unveiling and dissemination of dataabout his/her private (...) life. To study thesethreats, we distinguish between `content andstructure mining' and `usage mining.' Webcontent and structure mining is a cause forconcern when data published on the web in acertain context is mined and combined withother data for use in a totally differentcontext. Web usage mining raises privacyconcerns when web users are traced, and theiractions are analysed without their knowledge.Furthermore, both types of web mining are oftenused to create customer files with a strongtendency of judging and treating people on thebasis of group characteristics instead of ontheir own individual characteristics and merits(referred to as de-individualisation). Althoughthere are a variety of solutions toprivacy-problems, none of these solutionsoffers sufficient protection. Only a combinedsolution package consisting of solutions at anindividual as well as a collective level cancontribute to release some of the tensionbetween the advantages and the disadvantages ofweb mining. The values of privacy andindividuality should be respected and protectedto make sure that people are judged and treatedfairly. People should be aware of theseriousness of the dangers and continuouslydiscuss these ethical issues. This should be ajoint responsibility shared by web miners (bothadopters and developers), web users, andgovernments. (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 order to improve education and teaching methods and meet the “heart” needs of college students in the era of big data, this paper analyzes the application of data mining technology in college mental health education, and introduces database technology and decision tree algorithm to support college mental health work. This process verifies the feasibility of this kind of system with the help of an example. Using the test standards outlined in this document, 1.5 previous test tasks (...) were completed within the timeframe. During the system test, the error rate was 14% and the number of tests was 7%.However, the error rate in the development stage is 11%, which is lower than 19% of the old version. The error rate in the acceptance stage is 14%, which is lower than 5% of the old version. That is to say, most of the errors were found in time in the system analysis and design stage. 14% of the problems found in the development stage are basically small problems in the interface display, which do not need major changes. However, the old version also includes design defects found in the development stage, and only large-scale rewriting of the involved modules. In the research process, the work of mental health in Colleges and universities has been promoted. At this time, the law of psychological changes of college students has been summarized. Therefore, the support of data mining technology can better meet the needs of mental health education in Colleges and universities. (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)

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)

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)

: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)

Preventive vaccination is one of the greatest successes of modern medicine. The SARS-CoV-2 epidemic, during which vaccination is the main method of prevention against death and severe disease, gave rise to a resurgence of anti-vaccine movements. The aim of this study was to analyse the attitudes of students towards vaccination and the COVID-19 pandemic. The statistical analysis was performed with the use of the following data-mining methods: correspondence analysis and basket analysis. The obtained results show that students of (...) medicine are characterized with the highest level of knowledge. Students of other medical faculties, on the other hand, have a significantly less uniform views, as do students of non-medical faculties. (shrink)

International large-scale assessments, such as PISA, provide structured and static data. However, due to its extensive databases, several researchers place it as a reference in Big Data in Education. With the goal of exploring which factors at country, school and student level have a higher relevance in predicting student performance, this paper proposes an Educational Data Mining approach to detect and analyze factors linked to academic performance. To this end, we conducted a secondary data analysis (...) and built decision trees (C4.5 algorithm) to obtain a predictive model of school performance. Specifically, we selected as predictor variables a set of socioeconomic, process and outcome variables from PISA 2018 and other sources (World Bank, 2020). Since the unit of analysis were schools from all the countries included in PISA 2018 (n= 21,903), student and teacher predictor variables were imputed to the school database. Based on the available student performance scores in Reading, Math, and Science, we applied k-means clustering to obtain a categorized (three categories) target variable of global school performance. Results show the existence of two main branches in the decision tree, split according to the schools’ mean socioeconomic status (SES). While performance in high-SES schools is influenced by educational factors such as metacognitive strategies or achievement motivation, performance in low-SES schools is affected in greater measure by country-level socioeconomic indicators such as GDP, and individual educational indicators are relegated to a secondary level. Since these evidences are in line and delve into previous research, this work concludes by analyzing its potential contribution to support the decision making processes regarding educational policies. (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)

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (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)

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)

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)

"Data mining is not an invasion of privacy because access to data is only by machines, not by people": this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the (...) analysis will have to be made either by humans or by machines that understand. It concludes that the current data mining violates the right to privacy and should be subject to the standard legal constraints for access to private information by people. (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)

We argue that neural networks for semantic cognition, as proposed by Rogers & McClelland (R&M), do not acquire semantics and therefore cannot be the basis for a theory of semantic cognition. The reason is that the neural networks simply perform statistical categorization procedures, and these do not require any semantics for their successful operation. We conclude that this has severe consequences for the semantic cognition views of R&M.