Data mining is used wherever there is digital data available today. Notable "examples of data mining can be found throughout business, medicine, science, and surveillance.
Privacy concerns and ethics
While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to peoples' behavior (ethical and otherwise).
The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics. In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the "Total Information Awareness Program or in "ADVISE, has raised privacy concerns.
Data mining requires data preparation which can uncover information or patterns which may compromise confidentiality and privacy obligations. A common way for this to occur is through "data aggregation. Data aggregation involves combining data together (possibly from various sources) in a way that facilitates analysis (but that also might make identification of private, individual-level data deducible or otherwise apparent). This is not data mining per se, but a result of the preparation of data before – and for the purposes of – the analysis. The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous.
It is recommended that an individual is made aware of the following before data are collected:
- the purpose of the data collection and any (known) data mining projects;
- how the data will be used;
- who will be able to mine the data and use the data and their derivatives;
- the status of security surrounding access to the data;
- how collected data can be updated.
Data may also be modified so as to become anonymous, so that individuals may not readily be identified. However, even "de-identified"/"anonymized" data sets can potentially contain enough information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.
The inadvertent revelation of "personally identifiable information leading to the provider violates Fair Information Practices. This indiscretion can cause financial, emotional, or bodily harm to the indicated individual. In one instance of privacy violation, the patrons of Walgreens filed a lawsuit against the company in 2011 for selling prescription information to data mining companies who in turn provided the data to pharmaceutical companies.
Situation in Europe
Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers. However, the "U.S.-E.U. Safe Harbor Principles currently effectively expose European users to privacy exploitation by U.S. companies. As a consequence of "Edward Snowden's "Global surveillance disclosure, there has been increased discussion to revoke this agreement, as in particular the data will be fully exposed to the "National Security Agency, and attempts to reach an agreement have failed.["citation needed]
Situation in the United States
In the United States, privacy concerns have been addressed by the "US Congress via the passage of regulatory controls such as the "Health Insurance Portability and Accountability Act (HIPAA). The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses. According to an article in Biotech Business Week, "'[i]n practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena,' says the AAHC. More importantly, the rule's goal of protection through informed consent is undermined by the complexity of consent forms that are required of patients and participants, which approach a level of incomprehensibility to average individuals." This underscores the necessity for data anonymity in data aggregation and mining practices.
U.S. information privacy legislation such as HIPAA and the "Family Educational Rights and Privacy Act (FERPA) applies only to the specific areas that each such law addresses. Use of data mining by the majority of businesses in the U.S. is not controlled by any legislation.
Situation in Europe
Due to a lack of flexibilities in European copyright and "database law, the mining of in-copyright works such as "web mining without the permission of the copyright owner is not legal. Where a database is pure data in Europe there is likely to be no copyright, but database rights may exist so data mining becomes subject to regulations by the "Database Directive. On the recommendation of the "Hargreaves review this led to the UK government to amend its copyright law in 2014 to allow content mining as a "limitation and exception. Only the second country in the world to do so after Japan, which introduced an exception in 2009 for data mining. However, due to the restriction of the "Copyright Directive, the UK exception only allows content mining for non-commercial purposes. UK copyright law also does not allow this provision to be overridden by contractual terms and conditions. The "European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe. The focus on the solution to this legal issue being licences and not limitations and exceptions led to representatives of universities, researchers, libraries, civil society groups and "open access publishers to leave the stakeholder dialogue in May 2013.
Situation in the United States
By contrast to Europe, the flexible nature of US copyright law, and in particular "fair use means that content mining in America, as well as other fair use countries such as Israel, Taiwan and South Korea is viewed as being legal. As content mining is transformative, that is it does not supplant the original work, it is viewed as being lawful under fair use. For example, as part of the "Google Book settlement the presiding judge on the case ruled that Google's digitisation project of in-copyright books was lawful, in part because of the transformative uses that the digitisation project displayed - one being text and data mining.
Free open-source data mining software and applications
The following applications are available under free/open source licenses. Public access to application sourcecode is also available.
- "Carrot2: Text and search results clustering framework.
- "Chemicalize.org: A chemical structure miner and web search engine.
- "ELKI: A university research project with advanced "cluster analysis and "outlier detection methods written in the "Java language.
- "GATE: a "natural language processing and language engineering tool.
- "KNIME: The Konstanz Information Miner, a user friendly and comprehensive data analytics framework.
- "Massive Online Analysis (MOA): a real-time big data stream mining with concept drift tool in the "Java programming language.
- ML-Flex: A software package that enables users to integrate with third-party machine-learning packages written in any programming language, execute classification analyses in parallel across multiple computing nodes, and produce HTML reports of classification results.
- "MLPACK library: a collection of ready-to-use machine learning algorithms written in the "C++ language.
- "MEPX - cross platform tool for regression and classification problems based on a Genetic Programming variant.
- "NLTK ("Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the "Python language.
- "OpenNN: Open "neural networks library.
- "Orange: A component-based data mining and "machine learning software suite written in the "Python language.
- "R: A "programming language and software environment for "statistical computing, data mining, and graphics. It is part of the "GNU Project.
- "scikit-learn is an open source machine learning library for the Python programming language
- "Torch: An "open source "deep learning library for the "Lua programming language and "scientific computing framework with wide support for "machine learning algorithms.
- "UIMA: The UIMA (Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video – originally developed by IBM.
- "Weka: A suite of machine learning software applications written in the "Java programming language.
Proprietary data-mining software and applications
The following applications are available under proprietary licenses.
- "Angoss KnowledgeSTUDIO: data mining tool.
- "Clarabridge: text analytics product.
- "Vertica: data mining software provided by "Hewlett-Packard.
- "SPSS Modeler: data mining software provided by "IBM.
- KXEN Modeler: data mining tool provided by "KXEN Inc..
- "LIONsolver: an integrated software application for data mining, business intelligence, and modeling that implements the Learning and Intelligent OptimizatioN (LION) approach.
- "Megaputer Intelligence: data and text mining software is called PolyAnalyst.
- "Microsoft Analysis Services: data mining software provided by "Microsoft.
- "NetOwl: suite of multilingual text and entity analytics products that enable data mining.
- OpenText Big Data Analytics: Visual Data Mining & Predictive Analysis by "Open Text Corporation
- "Oracle Data Mining: data mining software by "Oracle Corporation.
- "PSeven: platform for automation of engineering simulation and analysis, multidisciplinary optimization and data mining provided by "DATADVANCE.
- "Qlucore Omics Explorer: data mining software.
- "RapidMiner: An environment for "machine learning and data mining experiments.
- "SAS Enterprise Miner: data mining software provided by the "SAS Institute.
- "STATISTICA Data Miner: data mining software provided by "StatSoft.
- "Tanagra: Visualisation-oriented data mining software, also for teaching.
Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, preferences and views of data miners. Some of these reports include:
- Hurwitz Victory Index: Report for Advanced Analytics as a market research assessment tool, it highlights both the diverse uses for advanced analytics technology and the vendors who make those applications possible.Recent-research
- 2011 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
- "Rexer Analytics Data Miner Surveys (2007–2015)
- "Forrester Research 2010 Predictive Analytics and Data Mining Solutions report
- "Gartner 2008 "Magic Quadrant" report
- Robert A. Nisbet's 2006 Three Part Series of articles "Data Mining Tools: Which One is Best For CRM?"
- Haughton et al.'s 2003 Review of Data Mining Software Packages in "The American Statistician
- Goebel & Gruenwald 1999 "A Survey of Data Mining a Knowledge Discovery Software Tools" in SIGKDD Explorations
- "Agent mining
- "Anomaly/outlier/change detection
- "Association rule learning
- "Bayesian networks
- "Cluster analysis
- "Decision trees
- "Ensemble learning
- "Factor analysis
- "Genetic algorithms
- "Intention mining
- "Learning classifier system
- "Multilinear subspace learning
- "Neural networks
- "Regression analysis
- "Sequence mining
- "Structured data analysis
- "Support vector machines
- "Text mining
- "Time series analysis
- Application domains
- Application examples
- Related topics
Data mining is about analyzing data; for information about extracting information out of data, see:
- "Data Mining Curriculum". "ACM "SIGKDD. 2006-04-30. Retrieved 2014-01-27.
- Clifton, Christopher (2010). "Encyclopædia Britannica: Definition of Data Mining". Retrieved 2010-12-09.
- "Hastie, Trevor; "Tibshirani, Robert; "Friedman, Jerome (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction". Retrieved 2012-08-07.
- "Fayyad, Usama; "Piatetsky-Shapiro, Gregory; Smyth, Padhraic (1996). "From Data Mining to Knowledge Discovery in Databases" (PDF). Retrieved 17 December 2008.
- "Han, Jiawei; Kamber, Micheline (2001). Data mining: concepts and techniques. "Morgan Kaufmann. p. 5. "ISBN "978-1-55860-489-6.
Thus, data mining should have been more appropriately named "knowledge mining from data," which is unfortunately somewhat long
- See e.g. OKAIRP 2005 Fall Conference, Arizona State University About.com: Datamining
- "Witten, Ian H.; Frank, Eibe; Hall, Mark A. (30 January 2011). Data Mining: Practical Machine Learning Tools and Techniques (3 ed.). Elsevier. "ISBN "978-0-12-374856-0.
- Bouckaert, Remco R.; Frank, Eibe; Hall, Mark A.; Holmes, Geoffrey; Pfahringer, Bernhard; Reutemann, Peter; "Witten, Ian H. (2010). "WEKA Experiences with a Java open-source project". Journal of Machine Learning Research. 11: 2533–2541.
the original title, "Practical machine learning", was changed ... The term "data mining" was [added] primarily for marketing reasons.
- Mena, Jesús (2011). Machine Learning Forensics for Law Enforcement, Security, and Intelligence. Boca Raton, FL: CRC Press (Taylor & Francis Group). "ISBN "978-1-4398-6069-4.
- "Piatetsky-Shapiro, Gregory; Parker, Gary (2011). "Lesson: Data Mining, and Knowledge Discovery: An Introduction". Introduction to Data Mining. KD Nuggets. Retrieved 30 August 2012.
- Fayyad, Usama (15 June 1999). "First Editorial by Editor-in-Chief". SIGKDD Explorations. 13 (1): 102. "doi:10.1145/2207243.2207269. Retrieved 27 December 2010.
- Kantardzic, Mehmed (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons. "ISBN "0-471-22852-4. "OCLC 50055336.
- "Gregory Piatetsky-Shapiro (2002) KDnuggets Methodology Poll, "Gregory Piatetsky-Shapiro (2004) KDnuggets Methodology Poll, "Gregory Piatetsky-Shapiro (2007) KDnuggets Methodology Poll, "Gregory Piatetsky-Shapiro (2014) KDnuggets Methodology Poll
- Óscar Marbán, Gonzalo Mariscal and Javier Segovia (2009); A Data Mining & Knowledge Discovery Process Model. In Data Mining and Knowledge Discovery in Real Life Applications, Book edited by: Julio Ponce and Adem Karahoca, "ISBN 978-3-902613-53-0, pp. 438–453, February 2009, I-Tech, Vienna, Austria.
- Lukasz Kurgan and Petr Musilek (2006); A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review. Volume 21 Issue 1, March 2006, pp 1–24, Cambridge University Press, New York, NY, USA "doi:10.1017/S0269888906000737
- Azevedo, A. and Santos, M. F. KDD, SEMMA and CRISP-DM: a parallel overview. In Proceedings of the IADIS European Conference on Data Mining 2008, pp 182–185.
- Hawkins, Douglas M (2004). "The problem of overfitting". Journal of chemical information and computer sciences. 44 (1): 1–12. "doi:10.1021/ci0342472.
- "Microsoft Academic Search: Top conferences in data mining". "Microsoft Academic Search.
- "Google Scholar: Top publications - Data Mining & Analysis". "Google Scholar.
- Proceedings, International Conferences on Knowledge Discovery and Data Mining, ACM, New York.
- SIGKDD Explorations, ACM, New York.
- Günnemann, Stephan; Kremer, Hardy; Seidl, Thomas (2011). "An extension of the PMML standard to subspace clustering models". Proceedings of the 2011 workshop on Predictive markup language modeling - PMML '11. p. 48. "doi:10.1145/2023598.2023605. "ISBN "978-1-4503-0837-3.
- Seltzer, William. "The Promise and Pitfalls of Data Mining: Ethical Issues" (PDF).
- Pitts, Chip (15 March 2007). "The End of Illegal Domestic Spying? Don't Count on It". Washington Spectator. Archived from the original on 2007-10-29.
- Taipale, Kim A. (15 December 2003). "Data Mining and Domestic Security: Connecting the Dots to Make Sense of Data". Columbia Science and Technology Law Review. 5 (2). "OCLC 45263753. "SSRN .
- Resig, John; Teredesai, Ankur (2004). "A Framework for Mining Instant Messaging Services". Proceedings of the 2004 SIAM DM Conference.
- Think Before You Dig: Privacy Implications of Data Mining & Aggregation, NASCIO Research Brief, September 2004
- Ohm, Paul. "Don't Build a Database of Ruin". Harvard Business Review.
- Darwin Bond-Graham, Iron Cagebook - The Logical End of Facebook's Patents, "Counterpunch.org, 2013.12.03
- Darwin Bond-Graham, Inside the Tech industry's Startup Conference, "Counterpunch.org, 2013.09.11
- AOL search data identified individuals, SecurityFocus, August 2006
- Kshetri, Nir (2014). "Big data׳s impact on privacy, security and consumer welfare". Telecommunications Policy. 38 (11): 1134–1145. "doi:10.1016/j.telpol.2014.10.002.
- Biotech Business Week Editors (June 30, 2008); BIOMEDICINE; HIPAA Privacy Rule Impedes Biomedical Research, Biotech Business Week, retrieved 17 November 2009 from LexisNexis Academic
- UK Researchers Given Data Mining Right Under New UK Copyright Laws. Archived June 9, 2014, at the "Wayback Machine. Out-Law.com. Retrieved 14 November 2014
- "Licences for Europe - Structured Stakeholder Dialogue 2013". European Commission. Retrieved 14 November 2014.
- "Text and Data Mining:Its importance and the need for change in Europe". Association of European Research Libraries. Retrieved 14 November 2014.
- "Judge grants summary judgment in favor of Google Books — a fair use victory". Lexology.com. Antonelli Law Ltd. Retrieved 14 November 2014.
- Mikut, Ralf; Reischl, Markus (September–October 2011). "Data Mining Tools". Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 1 (5): 431–445. "doi:10.1002/widm.24. Retrieved October 21, 2011.
- Karl Rexer, Heather Allen, & Paul Gearan (2011); Understanding Data Miners, Analytics Magazine, May/June 2011 (INFORMS: Institute for Operations Research and the Management Sciences).
- Kobielus, James; The Forrester Wave: Predictive Analytics and Data Mining Solutions, Q1 2010, Forrester Research, 1 July 2008
- Herschel, Gareth; Magic Quadrant for Customer Data-Mining Applications, Gartner Inc., 1 July 2008
- Nisbet, Robert A. (2006); Data Mining Tools: Which One is Best for CRM? Part 1, Information Management Special Reports, January 2006
- Haughton, Dominique; Deichmann, Joel; Eshghi, Abdolreza; Sayek, Selin; Teebagy, Nicholas; and Topi, Heikki (2003); A Review of Software Packages for Data Mining, The American Statistician, Vol. 57, No. 4, pp. 290–309
- Goebel, Michael; Gruenwald, Le (1999); A Survey of Data Mining and Knowledge Discovery Software Tools, SIGKDD Explorations, Vol. 1, Issue 1, pp. 20–33
- Cabena, Peter; Hadjnian, Pablo; Stadler, Rolf; Verhees, Jaap; Zanasi, Alessandro (1997); Discovering Data Mining: From Concept to Implementation, "Prentice Hall, "ISBN 0-13-743980-6
- M.S. Chen, J. Han, "P.S. Yu (1996) "Data mining: an overview from a database perspective". Knowledge and data Engineering, IEEE Transactions on 8 (6), 866–883
- Feldman, Ronen; Sanger, James (2007); The Text Mining Handbook, "Cambridge University Press, "ISBN 978-0-521-83657-9
- Guo, Yike; and Grossman, Robert (editors) (1999); High Performance Data Mining: Scaling Algorithms, Applications and Systems, "Kluwer Academic Publishers
- "Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques. Morgan kaufmann, 2006.
- "Hastie, Trevor, "Tibshirani, Robert and "Friedman, Jerome (2001); The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, "ISBN 0-387-95284-5
- Liu, Bing (2007); Web Data Mining: Exploring Hyperlinks, Contents and Usage Data, "Springer, "ISBN 3-540-37881-2
- Murphy, Chris (16 May 2011). "Is Data Mining Free Speech?". "InformationWeek. "UMB: 12.
- Nisbet, Robert; Elder, John; Miner, Gary (2009); Handbook of Statistical Analysis & Data Mining Applications, "Academic Press/Elsevier, "ISBN 978-0-12-374765-5
- Poncelet, Pascal; Masseglia, Florent; and Teisseire, Maguelonne (editors) (October 2007); "Data Mining Patterns: New Methods and Applications", Information Science Reference, "ISBN 978-1-59904-162-9
- Tan, Pang-Ning; Steinbach, Michael; and Kumar, Vipin (2005); Introduction to Data Mining, "ISBN 0-321-32136-7
- Theodoridis, Sergios; and Koutroumbas, Konstantinos (2009); Pattern Recognition, 4th Edition, Academic Press, "ISBN 978-1-59749-272-0
- Weiss, Sholom M.; and Indurkhya, Nitin (1998); Predictive Data Mining, "Morgan Kaufmann
- "Witten, Ian H.; Frank, Eibe; Hall, Mark A. (30 January 2011). Data Mining: Practical Machine Learning Tools and Techniques (3 ed.). Elsevier. "ISBN "978-0-12-374856-0. (See also "Free Weka software)
- Ye, Nong (2003); The Handbook of Data Mining, Mahwah, NJ: Lawrence Erlbaum
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