Data mining

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Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. [1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. [1] [2] [3] [4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. [5] Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. [1] The difference between data analysis and data mining is that data analysis is to summarize the history such as analyzing the effectiveness of a marketing campaign, in contrast, data mining focuses on using specific machine learning and statistical models to predict the future and discover the patterns among data. [6]

A data set is a collection of data. Most commonly a data set corresponds to the contents of a single database table, or a single statistical data matrix, where every column of the table represents a particular variable, and each row corresponds to a given member of the data set in question. The data set lists values for each of the variables, such as height and weight of an object, for each member of the data set. Each value is known as a datum. The data set may comprise data for one or more members, corresponding to the number of rows.

Machine learning branch of statistics and computer science, which studies algorithms and architectures that learn from observed facts

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

Statistics study of the collection, organization, analysis, interpretation, and presentation of data

Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation. In applying statistics to, for example, a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal". Statistics deals with all aspects of data, including the planning of data collection in terms of the design of surveys and experiments. See glossary of probability and statistics.

Contents

The term "data mining" is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. [7] It also is a buzzword [8] and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java [9] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. [10] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.

A misnomer is a name that is incorrectly applied to a thing. Misnomers often arise because something was named long before its correct nature was known, or because an earlier form of something has been replaced by something to which the name no longer applies. A misnomer may also be simply a word that someone uses incorrectly or misleadingly. The word "misnomer" does not mean "misunderstanding" or "popular misconception", and a number of misnomers remain in common usage — which is to say, the fact of a word being a misnomer does not necessarily make usage of the word incorrect.

A buzzword is a word or phrase, new or already existing, that becomes very popular for a period of time. Buzzwords often derive from technical terms yet often have much of the original technical meaning removed through fashionable use, being simply used to impress others; although such "buzzwords" may still have the full meaning when used in certain technical contexts. Buzzwords often originate in jargon, acronyms, or neologisms. Examples of overworked business buzzwords include synergy, vertical, dynamic, cyber and strategy; a common buzzword phrase is "think outside the box".

Information processing is the change (processing) of information in any manner detectable by an observer. As such, it is a process that describes everything that happens (changes) in the universe, from the falling of a rock to the printing of a text file from a digital computer system. In the latter case, an information processor is changing the form of presentation of that text file. Information processing may more specifically be defined in terms used by, Claude E. Shannon as the conversion of latent information into manifest information. Latent and manifest information is defined through the terms of equivocation, dissipation, and transformation.

The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.

Cluster analysis Task of grouping a set of objects so that objects in the same group (or cluster) are more similar to each other than to those in other clusters

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.

Anomaly detection

In data mining, anomaly detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

Sequential pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence. It is usually presumed that the values are discrete, and thus time series mining is closely related, but usually considered a different activity. Sequential pattern mining is a special case of structured data mining.

The related terms data dredging , data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

Data dredging use of data mining to uncover patterns in data that can be presented as statistically significant

Data dredging is the misuse of data analysis to find patterns in data that can be presented as statistically significant when in fact there is no real underlying effect. This is done by performing many statistical tests on the data and only paying attention to those that come back with significant results, instead of stating a single hypothesis about an underlying effect before the analysis and then conducting a single test for it.

Etymology

In the 1960s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term "data mining" was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Economic Studies 1983. Lovell indicates that the practice "masquerades under a variety of aliases, ranging from "experimentation" (positive) to "fishing" or "snooping" (negative). [11]

Michael R. Lovell is an American engineer, educator, and President of Marquette University. Lovell was named the first lay president in the history of Marquette on March 26, 2014, and started at Marquette on July 1, 2014.

The term data mining appeared around 1990 in the database community, generally with positive connotations. For a short time in 1980s, a phrase "database mining"™, was used, but since it was trademarked by HNC, a San Diego-based company, to pitch their Database Mining Workstation; [12] researchers consequently turned to data mining. Other terms used include data archaeology, information harvesting, information discovery, knowledge extraction, etc. Gregory Piatetsky-Shapiro coined the term "knowledge discovery in databases" for the first workshop on the same topic (KDD-1989) and this term became more popular in AI and machine learning community. However, the term data mining became more popular in the business and press communities. [13] Currently, the terms data mining and knowledge discovery are used interchangeably.

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. More specifically, Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”. Colloquially, the term "artificial intelligence" is used to describe machines that mimic "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".

In the academic community, the major forums for research started in 1995 when the First International Conference on Data Mining and Knowledge Discovery (KDD-95) was started in Montreal under AAAI sponsorship. It was co-chaired by Usama Fayyad and Ramasamy Uthurusamy. A year later, in 1996, Usama Fayyad launched the journal by Kluwer called Data Mining and Knowledge Discovery as its founding editor-in-chief. Later he started the SIGKDD Newsletter SIGKDD Explorations. [14] The KDD International conference became the primary highest quality conference in data mining with an acceptance rate of research paper submissions below 18%. The journal Data Mining and Knowledge Discovery is the primary research journal of the field.

Background

The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees and decision rules (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns [15] in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever larger data sets.

Process

The knowledge discovery in databases (KDD) process is commonly defined with the stages:

  1. Selection
  2. Pre-processing
  3. Transformation
  4. Data mining
  5. Interpretation/evaluation. [5]

It exists, however, in many variations on this theme, such as the Cross-industry standard process for data mining (CRISP-DM) which defines six phases:

  1. Business understanding
  2. Data understanding
  3. Data preparation
  4. Modeling
  5. Evaluation
  6. Deployment

or a simplified process such as (1) Pre-processing, (2) Data Mining, and (3) Results Validation.

Polls conducted in 2002, 2004, 2007 and 2014 show that the CRISP-DM methodology is the leading methodology used by data miners. [16] The only other data mining standard named in these polls was SEMMA. However, 3–4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models, [17] [18] and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008. [19]

Pre-processing

Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.

Data mining

Data mining involves six common classes of tasks: [5]

Results validation

An example of data produced by data dredging through a bot operated by statistician Tyler Vigen, apparently showing a close link between the best word winning a spelling bee competition and the number of people in the United States killed by venomous spiders. The similarity in trends is obviously a coincidence. Spurious correlations - spelling bee spiders.svg
An example of data produced by data dredging through a bot operated by statistician Tyler Vigen, apparently showing a close link between the best word winning a spelling bee competition and the number of people in the United States killed by venomous spiders. The similarity in trends is obviously a coincidence.

Data mining can unintentionally be misused, and can then produce results which appear to be significant; but which do not actually predict future behaviour and cannot be reproduced on a new sample of data and bear little use. Often this results from investigating too many hypotheses and not performing proper statistical hypothesis testing. A simple version of this problem in machine learning is known as overfitting, but the same problem can arise at different phases of the process and thus a train/test split - when applicable at all - may not be sufficient to prevent this from happening. [20]

The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the data mining algorithms are necessarily valid. It is common for the data mining algorithms to find patterns in the training set which are not present in the general data set. This is called overfitting. To overcome this, the evaluation uses a test set of data on which the data mining algorithm was not trained. The learned patterns are applied to this test set, and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish "spam" from "legitimate" emails would be trained on a training set of sample e-mails. Once trained, the learned patterns would be applied to the test set of e-mails on which it had not been trained. The accuracy of the patterns can then be measured from how many e-mails they correctly classify. A number of statistical methods may be used to evaluate the algorithm, such as ROC curves.

If the learned patterns do not meet the desired standards, subsequently it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns do meet the desired standards, then the final step is to interpret the learned patterns and turn them into knowledge.

Research

The premier professional body in the field is the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining (SIGKDD). [21] [22] Since 1989, this ACM SIG has hosted an annual international conference and published its proceedings, [23] and since 1999 it has published a biannual academic journal titled "SIGKDD Explorations". [24]

Computer science conferences on data mining include:

Data mining topics are also present on many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases

Standards

There have been some efforts to define standards for the data mining process, for example the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006, but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.

For exchanging the extracted models – in particular for use in predictive analytics  – the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover (for example) subspace clustering have been proposed independently of the DMG. [25]

Notable uses

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). [26]

The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics. [27] 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. [28] [29]

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). [30] 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. [31] [32] [33]

It is recommended that an individual is made aware of the following before data are collected: [30]

Data may also be modified so as to become anonymous, so that individuals may not readily be identified. [30] 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. [34]

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. [35]

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 approach a level of incomprehensibility to average individuals." [36] 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 [37] 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. [38] 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. [39]

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. [40]

Software

Free open-source data mining software and applications

The following applications are available under free/open source licenses. Public access to application source code is also available.

Proprietary data-mining software and applications

The following applications are available under proprietary licenses.

Marketplace surveys

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:

See also

Methods
Application domains
Application examples
Related topics

Data mining is about analyzing data; for information about extracting information out of data, see:

Other resources

Related Research Articles

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Gregory Piatetsky-Shapiro data scientist and co-founder of KDD conferences and ACM SIGKDD association

Gregory I. Piatetsky-Shapiro is a data scientist and the co-founder of the KDD conferences, and co-founder and past chair of the Association for Computing Machinery SIGKDD group for Knowledge Discovery, Data Mining and Data Science. He is the founder and president of KDnuggets, a discussion and learning website for Business Analytics, Data Mining and Data Science.

References

  1. 1 2 3 "Data Mining Curriculum". ACM SIGKDD. 2006-04-30. Retrieved 2014-01-27.
  2. Clifton, Christopher (2010). "Encyclopædia Britannica: Definition of Data Mining" . Retrieved 2010-12-09.
  3. Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction". Archived from the original on 2009-11-10. Retrieved 2012-08-07.
  4. Han, Kamber, Pei, Jaiwei, Micheline, Jian (June 9, 2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann. ISBN   978-0-12-381479-1.CS1 maint: Multiple names: authors list (link)
  5. 1 2 3 Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic (1996). "From Data Mining to Knowledge Discovery in Databases" (PDF). Retrieved 17 December 2008.
  6. Olson, D. L. (2007). Data mining in business services. Service Business, 1(3), 181-193. doi:10.1007/s11628-006-0014-7
  7. 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
  8. OKAIRP 2005 Fall Conference, Arizona State University Archived 2014-02-01 at the Wayback Machine
  9. 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.
  10. 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.
  11. Lovell, Michael C. (1983). "Data Mining". The Review of Economics and Statistics. 65 (1): 1–12. doi:10.2307/1924403. JSTOR   1924403.
  12. 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.
  13. Piatetsky-Shapiro, Gregory; Parker, Gary (2011). "Lesson: Data Mining, and Knowledge Discovery: An Introduction". Introduction to Data Mining. KD Nuggets. Retrieved 30 August 2012.
  14. 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.
  15. Kantardzic, Mehmed (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons. ISBN   978-0-471-22852-3. OCLC   50055336.
  16. 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
  17. 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.
  18. 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
  19. Azevedo, A. and Santos, M. F. KDD, SEMMA and CRISP-DM: a parallel overview Archived 2013-01-09 at the Wayback Machine . In Proceedings of the IADIS European Conference on Data Mining 2008, pp 182–185.
  20. Hawkins, Douglas M (2004). "The problem of overfitting". Journal of Chemical Information and Computer Sciences. 44 (1): 1–12. doi:10.1021/ci0342472. PMID   14741005.
  21. "Microsoft Academic Search: Top conferences in data mining". Microsoft Academic Search.
  22. "Google Scholar: Top publications - Data Mining & Analysis". Google Scholar.
  23. Proceedings Archived 2010-04-30 at the Wayback Machine , International Conferences on Knowledge Discovery and Data Mining, ACM, New York.
  24. SIGKDD Explorations, ACM, New York.
  25. 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.
  26. Seltzer, William (2005). "The Promise and Pitfalls of Data Mining: Ethical Issues" (PDF). ASA Section on Government Statistics. American Statistical Association.
  27. 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.
  28. 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   546782 .
  29. Resig, John. "A Framework for Mining Instant Messaging Services" (PDF). Retrieved 16 March 2018.
  30. 1 2 3 Think Before You Dig: Privacy Implications of Data Mining & Aggregation Archived 2008-12-17 at the Wayback Machine , NASCIO Research Brief, September 2004
  31. Ohm, Paul. "Don't Build a Database of Ruin". Harvard Business Review.
  32. Darwin Bond-Graham, Iron Cagebook - The Logical End of Facebook's Patents, Counterpunch.org, 2013.12.03
  33. Darwin Bond-Graham, Inside the Tech industry's Startup Conference, Counterpunch.org, 2013.09.11
  34. AOL search data identified individuals, SecurityFocus, August 2006
  35. Kshetri, Nir (2014). "Big data׳s impact on privacy, security and consumer welfare" (PDF). Telecommunications Policy. 38 (11): 1134–1145. doi:10.1016/j.telpol.2014.10.002.
  36. Biotech Business Week Editors (June 30, 2008); BIOMEDICINE; HIPAA Privacy Rule Impedes Biomedical Research, Biotech Business Week, retrieved 17 November 2009 from LexisNexis Academic
  37. 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
  38. "Licences for Europe - Structured Stakeholder Dialogue 2013". European Commission. Retrieved 14 November 2014.
  39. "Text and Data Mining:Its importance and the need for change in Europe". Association of European Research Libraries. Retrieved 14 November 2014.
  40. "Judge grants summary judgment in favor of Google Books — a fair use victory". Lexology.com. Antonelli Law Ltd. Retrieved 14 November 2014.
  41. Karl Rexer, Heather Allen, & Paul Gearan (2011); Understanding Data Miners, Analytics Magazine, May/June 2011 (INFORMS: Institute for Operations Research and the Management Sciences).
  42. 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.
  43. Kobielus, James; The Forrester Wave: Predictive Analytics and Data Mining Solutions, Q1 2010, Forrester Research, 1 July 2008
  44. Herschel, Gareth; Magic Quadrant for Customer Data-Mining Applications, Gartner Inc., 1 July 2008
  45. Nisbet, Robert A. (2006); Data Mining Tools: Which One is Best for CRM? Part 1, Information Management Special Reports, January 2006
  46. 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
  47. Goebel, Michael; Gruenwald, Le (June 1999). "A Survey of Data Mining and Knowledge Discovery Software Tools" (PDF). SIGKDD Explorations. 1 (1): 20–33.

Further reading