data mining machine learning anddata mining problems classification clustering regression anomaly detection automl association rules reinforcement learning structured prediction feature engineering feature learning online learning semisupervised learning unsupervised learning learning to rank grammar induction supervised learning classification regression decision trees ensembles bagging boosting random forest knn linear regression naive bayes artificial neural networks logistic regression perceptron relevance vector machine rvm support vector machine svm clustering birch cure hierarchical kmeans expectationmaximization em dbscan optics meanshift dimensionality reduction factor analysis cca ica lda nmf pca tsne structured prediction graphical models bayes net conditional random field hidden markov anomaly detection knn local outlier factor artificial neural networks autoencoder deep learning multilayer perceptron rnn lstm gru restricted boltzmann machine som convolutional neural network unet reinforcement learning qlearning sarsa temporal difference td theory biasvariance dilemma computational learning theory empirical risk minimization occam learning pac learning statistical learning vc theory machinelearning venues nips icml ml jmlr arxivcslg glossary of artificial intelligence glossary of artificial intelligence related articles list of datasets for machinelearning research outline of machine learning machine learning portalvte data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning statistics and database systems 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 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 semiautomatic 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 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 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 apriori 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 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 diegobased company to pitch their database mining workstation currently the terms data mining and knowledge discovery are used interchangeably in the academic community the major forums for research started in 1995 when the first international conference on data mining and knowledge discovery kdd95 was started in montreal under aaai sponsorship it was cochaired 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 editorinchief later he started the sigkdd newsletter sigkdd explorations 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 handson 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 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 selection preprocessing transformation data mining interpretationevaluation it exists however in many variations on this theme such as the cross industry standard process for data mining crispdm which defines six phases business understanding data understanding data preparation modeling evaluation deployment or a simplified process such as 1 preprocessing 2 data mining and 3 results validation polls conducted in 2002 2004 2007 and 2014 show that the crispdm methodology is the leading methodology used by data miners preprocessing 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 preprocessing 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 anomaly detection outlierchangedeviation detection the identification of unusual data records that might be interesting or data errors that require further investigation association rule learning dependency modelling 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 email program might attempt to classify an email as legitimate or as spam regression attempts to find a function which 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 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 traintest split when applicable at all may not be sufficient to prevent this from happening 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 emails once trained the learned patterns would be applied to the test set of emails on which it had not been trained the accuracy of the patterns can then be measured from how many emails 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 reevaluate and change the preprocessing 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 machinerys acm special interest group sig on knowledge discovery and data mining sigkdd 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 kdd conference acm sigkdd conference on knowledge discovery and data mining data mining topics are also present on many data managementdatabase 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 crispdm 10 and the 2004 java data mining standard jdm 10 development on successors to these processes crispdm 20 and jdm 20 was active in 2006 but has stalled since jdm 20 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 xmlbased 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 notable uses examples of data mining see also categoryapplied data mining 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 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 individuallevel data deducible or otherwise apparent 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 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 useu safe harbor principles currently effectively expose european users to privacy exploitation by us companies as a consequence of edward snowdens 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 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 this underscores the necessity for data anonymity in data aggregation and mining practices us 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 us is not controlled by any legislation copyright law situation in europe due to a lack of flexibilities in european copyright and database law the mining of incopyright 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 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 googles digitisation project of incopyright books was lawful in part because of the transformative uses that the digitisation project displayed one being text and data mining software see also categorydata mining and machine learning software free opensource data mining software and applications the following applications are available under freeopen source licenses public access to application source code is also available carrot2 text and search results clustering framework chemicalizeorg 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 realtime big data stream mining with concept drift tool in the java programming language mepx cross platform tool for regression and classification problems based on a genetic programming variant mlflex a software package that enables users to integrate with thirdparty machinelearning packages written in any programming language execute classification analyses in parallel across multiple computing nodes and produce html reports of classification results mlpack a collection of readytouse 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 python language opennn open neural networks library orange a componentbased 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 scikitlearn 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 datamining software and applications the following applications are available under proprietary licenses angoss knowledgestudio data mining tool clarabridge text analytics product 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 spss modeler data mining software provided by ibm statistica data miner data mining software provided by statsoft tanagra visualisationoriented data mining software also for teaching vertica data mining software provided by hewlettpackard 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 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 possiblerecentresearch rexer analytics data miner surveys 20072015 2011 wiley interdisciplinary reviews data mining and knowledge discovery forrester research 2010 predictive analytics and data mining solutions report gartner 2008 magic quadrant report robert a nisbets 2006 three part series of articles data mining tools which one is best for crm haughton et als 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 methods agent mining anomalyoutlierchange 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 analytics behavior informatics big data bioinformatics business intelligence data analysis data warehouse decision support system domain driven data mining drug discovery exploratory data analysis predictive analytics web mining application examples examples of data mining see also categoryapplied data mining automatic number plate recognition in the united kingdom customer analytics educational data mining national security agency quantitative structureactivity relationship surveillance mass surveillance eg stellar wind related topics data mining is about analyzing data for information about extracting information out of data see data integration data transformation electronic discovery information extraction information integration namedentity recognition profiling information science psychometrics social media mining surveillance capitalism web scraping other resources international journal of data warehousing and mining cabena peter hadjnian pablo stadler rolf verhees jaap zanasi alessandro 1997 discovering data mining from concept to implementation prentice hall isbn0137439806 ms chen j han ps yu 1996 data mining an overview from a database perspective knowledge and data engineering ieee transactions on 8 6 866883 feldman ronen sanger james 2007 the text mining handbook cambridge university press isbn9780521836579 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 isbn0387952845 liu bing 2007 web data mining exploring hyperlinks contents and usage data springer isbn3540378812 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 academic presselsevier isbn9780123747655 poncelet pascal masseglia florent and teisseire maguelonne editors october 2007 data mining patterns new methods and applications information science reference isbn9781599041629 tan pangning steinbach michael and kumar vipin 2005 introduction to data mining isbn0321321367 theodoridis sergios and koutroumbas konstantinos 2009 pattern recognition 4th edition academic press isbn9781597492720 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 isbn9780123748560 see also free weka software ye nong 2003 the handbook of data mining mahwah nj lawrence erlbaum wikimedia commons has media related to data mining knowledge discovery software at curlie data mining tool vendors at curlie vtedata analysis archaeology cleansing collection compression corruption curation degradation editing farming format management fusion integration integrity library loss management migration mining preprocessing preservation protection privacy recovery reduction retention quality science scraping scrubbing security stewardship storage validation warehouse wranglingmunging vtedata warehousecreating the data warehouseconcepts database dimension dimensional modeling fact olap star schema snowflake schema reverse star schema aggregate variants anchor modeling columnoriented dbms data vault modeling holap molap rolap operational data store elements data dictionarymetadata data mart sixth normal form surrogate key fact fact table earlyarriving fact measure dimension dimension table degenerate slowly changing filling extracttransformload etl extract transform load using the data warehouseconcepts business intelligence dashboard data mining decision support system dss olap cube data warehouse automation languages data mining extensions dmx multidimensional expressions mdx xml for analysis xmla tools business intelligence software reporting software spreadsheet relatedpeople bill inmon ralph kimball products comparison of olap servers data warehousing products and their producers vtemajor fields of computer sciencenote this template roughly follows the 2012 acm computing classification systemhardware printed circuit board peripheral integrated circuit very large scale integration systems on chip socs energy consumption green computing electronic design automation hardware acceleration computer systemsorganization computer architecture embedded system realtime computing dependability networks network architecture network protocol network components network scheduler network performance evaluation network service software organization interpreter middleware virtual machine operating system software quality software notationsand tools programming paradigm programming language compiler domainspecific language modeling language software framework integrated development environment software configuration management software library software repository software development software development process requirements analysis software design software construction software deployment software maintenance programming team opensource model theory of computation model of computation formal language automata theory computational complexity theory logic semantics algorithms algorithm design analysis of algorithms algorithmic efficiency randomized algorithm computational geometry mathematicsof computing discrete mathematics probability statistics mathematical software information theory mathematical analysis numerical analysis informationsystems database management system information storage systems enterprise information system social information systems geographic information system decision support system process control system multimedia information system data mining digital library computing platform digital marketing world wide web information retrieval security cryptography formal methods security services intrusion detection system hardware security network security information security application security humancomputerinteraction interaction design social computing ubiquitous computing visualization accessibility concurrency concurrent computing parallel computing distributed computing multithreading multiprocessing artificialintelligence natural language processing knowledge representation and reasoning computer vision automated planning and scheduling search methodology control method philosophy of artificial intelligence distributed artificial intelligence machine learning supervised learning unsupervised learning reinforcement learning multitask learning crossvalidation graphics animation rendering image manipulation graphics processing unit mixed reality virtual reality image compression solid modeling appliedcomputing ecommerce enterprise software computational mathematics computational physics computational chemistry computational biology computational social science computational engineering computational healthcare digital art electronic publishing cyberwarfare electronic voting video games word processing operations research educational technology document management book category portal wikiproject commons authority control gnd 44286545 ndl 00948240 