Data mining
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Data mining is the process of extracting and discovering patterns in large
The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.[6] It also is a buzzword[7] 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. 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.
The actual data mining task is the semi-
The difference between
The related terms
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
Lovell indicates that the practice "masquerades under a variety of aliases, ranging from "experimentation" (positive) to "fishing" or "snooping" (negative).The term data mining appeared around 1990 in the database community, with generally positive connotations. For a short time in 1980s, the phrase "database mining"™, was used, but since it was trademarked by HNC, a
Background
The manual extraction of patterns from
Process
The knowledge discovery in databases (KDD) process is commonly defined with the stages:
- Selection
- Pre-processing
- Transformation
- Data mining
- 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:
- Business understanding
- Data understanding
- Data preparation
- Modeling
- Evaluation
- 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.[15][16][17][18]
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,[19] and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.[20]
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 mining
Data mining involves six common classes of tasks:[5]
- Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation due to being out of standard range.
- Association rule learning (dependency modeling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
- Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
- Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".
- Regression – attempts to find a function that models the data with the least error that is, for estimating the relationships among data or datasets.
- Summarization – providing a more compact representation of the data set, including visualization and report generation.
Results validation
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 algorithms are necessarily valid. It is common for data mining algorithms to find patterns in the training set which are not present in the general data set. This is called
If the learned patterns do not meet the desired standards, 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
Computer science conferences on data mining include:
- CIKM Conference – ACM Conference on Information and Knowledge Management
- European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
- Conference on Knowledge Discovery and Data Mining
Data mining topics are also present in 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
For exchanging the extracted models—in particular for use in
Notable uses
Data mining is used wherever there is digital data available. Notable examples of data mining can be found throughout business, medicine, science, finance, construction, 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 user behavior (ethical and otherwise).[27]
The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics.[28] 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.[29][30]
Data mining requires data preparation which uncovers information or patterns which compromise
It is recommended[according to whom?] to be aware of the following before data are collected:[31]
- 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.[31] However, even "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.[33]
The inadvertent revelation of
Situation in Europe
In the United Kingdom in particular there have been cases of corporations using data mining as a way to target certain groups of customers forcing them to pay unfairly high prices. These groups tend to be people of lower socio-economic status who are not savvy to the ways they can be exploited in digital market places.[36]
Situation in the United States
In the United States, privacy concerns have been addressed by the
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. The use of data mining by the majority of businesses in the U.S. is not controlled by any legislation.
Copyright law
Situation in Europe
Under
The European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe.[40] The focus on the solution to this legal issue, such as licensing rather than 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.[41]
Situation in the United States
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.
- 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 Javaprogramming language.
- MEPX: cross-platform tool for regression and classification problems based on a Genetic Programming variant.
- mlpack: a collection of ready-to-use machine learning algorithms written in the C++ language.
- NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Pythonlanguage.
- neural networkslibrary.
- Orange: A component-based data mining and machine learning software suite written in the Python language.
- PSPP: Data mining and statistics software under the GNU Project similar to SPSS
- R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU Project.
- scikit-learn: An open-source machine learning library for the Python programming language;
- scientific computing framework with wide support for machine learningalgorithms.
- 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 Javaprogramming language.
Proprietary data-mining software and applications
The following applications are available under proprietary licenses.
- Angoss KnowledgeSTUDIO: data mining tool
- LIONsolver: an integrated software application for data mining, business intelligence, and modeling that implements the Learning and Intelligent OptimizatioN (LION) approach.
- PolyAnalyst: data and text mining software by Megaputer Intelligence.
- Microsoft Analysis Services: data mining software provided by Microsoft.
- NetOwl: suite of multilingual text and entity analytics products that enable data mining.
- 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.
- SPSS Modeler: data mining software provided by IBM.
- STATISTICA Data Miner: data mining software provided by StatSoft.
- Tanagra: Visualisation-oriented data mining software, also for teaching.
- Vertica: data mining software provided by Hewlett-Packard.
- Google Cloud Platform: automated custom ML models managed by Google.
- Amazonfor creating & productionising custom ML models.
See also
- Methods
- Agent mining
- Anomaly/outlier/change detection
- Association rule learning
- Bayesian networks
- Classification
- 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
- Automatic number plate recognition in the United Kingdom
- Customer analytics
- Educational data mining
- National Security Agency
- Quantitative structure–activity relationship
- Stellar Wind)
- Related topics
For more information about extracting information out of data (as opposed to analyzing data), see:
- Other resources
References
- ^ SIGKDD. 2006-04-30. Archivedfrom the original on 2013-10-14. Retrieved 2014-01-27.
- ^ Clifton, Christopher (2010). "Encyclopædia Britannica: Definition of Data Mining". Archived from the original on 2011-02-05. Retrieved 2010-12-09.
- ^ 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.
- ISBN 978-0-12-381479-1.
- ^ a b c Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic (1996). "From Data Mining to Knowledge Discovery in Databases" (PDF). Archived (PDF) from the original on 2022-10-09. Retrieved 17 December 2008.
- ISBN 978-1-55860-489-6.
Thus, data mining should have been more appropriately named "knowledge mining from data," which is unfortunately somewhat long
- ^ OKAIRP 2005 Fall Conference, Arizona State University Archived 2014-02-01 at the Wayback Machine
- JSTOR 1924403.
- ISBN 1-85278-461-X.
- 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. Archived from the original on 30 August 2012. Retrieved 30 August 2012.
- from the original on 2023-07-02. Retrieved 2021-09-04.
- OCLC 50055336.
- KDnuggets. 2002. Archivedfrom the original on 16 January 2017. Retrieved 29 December 2023.
- KDnuggets. 2004. Archivedfrom the original on 8 February 2017. Retrieved 29 December 2023.
- KDnuggets. 2007. Archivedfrom the original on 17 November 2012. Retrieved 29 December 2023.
- KDnuggets. 2014. Archivedfrom the original on 1 August 2016. Retrieved 29 December 2023.
- ^ 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.
- S2CID 12440383.
- ^ "Microsoft Academic Search: Top conferences in data mining". Microsoft Academic Search. Archived from the original on 2014-11-19. Retrieved 2014-06-13.
- ^ "Google Scholar: Top publications - Data Mining & Analysis". Google Scholar. Archived from the original on 2023-02-10. Retrieved 2022-06-11.
- ^ Proceedings Archived 2010-04-30 at the Wayback Machine, International Conferences on Knowledge Discovery and Data Mining, ACM, New York.
- ^ SIGKDD Explorations Archived 2010-07-29 at the Wayback Machine, ACM, New York.
- S2CID 14967969.
- ^ Seltzer, William (2005). "The Promise and Pitfalls of Data Mining: Ethical Issues" (PDF). ASA Section on Government Statistics. American Statistical Association. Archived (PDF) from the original on 2022-10-09.
- ^ Pitts, Chip (15 March 2007). "The End of Illegal Domestic Spying? Don't Count on It". Washington Spectator. Archived from the original on 2007-11-28.
- SSRN 546782. Archived from the originalon 5 November 2014. Retrieved 21 April 2004.
- ^ Resig, John. "A Framework for Mining Instant Messaging Services" (PDF). Archived (PDF) from the original on 2022-10-09. Retrieved 16 March 2018.
- ^ a b c Think Before You Dig: Privacy Implications of Data Mining & Aggregation Archived 2008-12-17 at the Wayback Machine, NASCIO Research Brief, September 2004
- ^ Ohm, Paul. "Don't Build a Database of Ruin". Harvard Business Review.
- ^ AOL search data identified individuals Archived 2010-01-06 at the Wayback Machine, SecurityFocus, August 2006
- (PDF) from the original on 2018-06-19. Retrieved 2018-04-20.
- ^ Weiss, Martin A.; Archick, Kristin (19 May 2016). "U.S.–E.U. Data Privacy: From Safe Harbor to Privacy Shield". Washington, D.C. Congressional Research Service. p. 6. R44257. Archived from the original (PDF) on 9 April 2020. Retrieved 9 April 2020.
On October 6, 2015, the CJEU ... issued a decision that invalidated Safe Harbor (effective immediately), as currently implemented.
- ^ Parker, George (2018-09-30). "UK companies targeted for using big data to exploit customers". Financial Times. Archived from the original on 2022-12-10. Retrieved 2022-12-04.
- ^ 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
- ^ "Fedlex". Archived from the original on 2021-12-16. Retrieved 2021-12-16.
- ^ "Licences for Europe – Structured Stakeholder Dialogue 2013". European Commission. Archived from the original on 23 March 2013. Retrieved 14 November 2014.
- ^ "Text and Data Mining:Its importance and the need for change in Europe". Association of European Research Libraries. Archived from the original on 29 November 2014. Retrieved 14 November 2014.
- ^ "Judge grants summary judgment in favor of Google Books – a fair use victory". Lexology.com. Antonelli Law Ltd. 19 November 2013. Archived from the original on 29 November 2014. Retrieved 14 November 2014.
Further reading
- Cabena, Peter; Hadjnian, Pablo; Stadler, Rolf; Verhees, Jaap; Zanasi, Alessandro (1997); Discovering Data Mining: From Concept to Implementation, ISBN 0-13-743980-6
- M.S. Chen, J. Han, P.S. Yu (1996) "Data mining: an overview from a database perspective Archived 2016-03-03 at the Wayback Machine". Knowledge and data Engineering, IEEE Transactions on 8 (6), 866–883
- Feldman, Ronen; Sanger, James (2007); The Text Mining Handbook, 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.
- ISBN 0-387-95284-5
- ISBN 3-540-37881-2
- Murphy, Chris (16 May 2011). "Is Data Mining Free Speech?". InformationWeek: 12.
- Nisbet, Robert; Elder, John; Miner, Gary (2009); Handbook of Statistical Analysis & Data Mining Applications, 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
- Free Weka software)
- Ye, Nong (2003); The Handbook of Data Mining, Mahwah, NJ: Lawrence Erlbaum
External links
- Knowledge Discovery Software at Curlie
- Data Mining Tool Vendors at Curlie