The actual data mining task is the semi-automatic or automatic analysis of massive quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), anddependencies (association rule mining,sequential pattern mining). This usually involves using database techniques such asspatial 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 andpredictive 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 adecision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, although they do belong to the overall KDD process as additional steps.
The difference betweendata analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of amarketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.[8]
The related termsdata dredging,data fishing, anddata 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.
In the 1960s, statisticians and economists used terms likedata fishing ordata dredging to refer to what they considered the bad practice of analyzing data without ana-priori hypothesis. The term "data mining" was used in a similarly critical way by economistMichael Lovell in an article published in theReview of Economic Studies in 1983.[9][10] Lovell indicates that the practice "masquerades under a variety of aliases, ranging from "experimentation" (positive) to "fishing" or "snooping" (negative).
The termdata 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, aSan Diego–based company, to pitch their Database Mining Workstation;[11] researchers consequently turned todata mining. Other terms used includedata 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)[12] and this term became more popular in theAI andmachine learning communities. However, the term data mining became more popular in the business and press communities.[13] Currently, the termsdata mining andknowledge discovery are used interchangeably.
The manual extraction of patterns fromdata has occurred for centuries. Early methods of identifying patterns in data includeBayes' theorem (1700s) andregression analysis (1800s).[14] The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. Asdata 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, specially in the field of machine learning, such asneural networks,cluster analysis,genetic algorithms (1950s),decision trees anddecision rules (1960s), andsupport 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 fromapplied statistics and artificial intelligence (which usually provide the mathematical background) todatabase 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.
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][17][18][19]
The only other data mining standard named in these polls wasSEMMA. However, 3–4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models,[20] and Azevedo and Santos conducted a comparison of CRISP-DM andSEMMA in 2008.[21]
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 adata mart ordata warehouse. Pre-processing is essential to analyze themultivariate data sets before data mining. The target set is then cleaned.Data cleaning removes the observations containingnoise and those withmissing data.
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 asmarket 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.
An example of data produced bydata 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
Data mining can unintentionally be misused, producing results that appear to be significant but which do not actually predict future behavior and cannot bereproduced on a new sample of data, therefore bearing little use. This is sometimes caused by investigating too many hypotheses and not performing properstatistical hypothesis testing. A simple version of this problem inmachine learning is known asoverfitting, 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.[22]
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 calledoverfitting. To overcome this, the evaluation uses atest 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" e-mails would be trained on atraining set of sample e-mails. Once trained, the learned patterns would be applied to the test set of e-mails on which it hadnot been trained. The accuracy of the patterns can then be measured from how many e-mails they correctly classify. Several statistical methods may be used to evaluate the algorithm, such asROC curves.
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.
The premier professional body in the field is theAssociation for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining (SIGKDD).[23][24] Since 1989, this ACM SIG has hosted an annual international conference and published its proceedings,[25] and since 1999 it has published a biannualacademic journal titled "SIGKDD Explorations".[26]
Computer science conferences on data mining include:
There have been some efforts to define standards for the data mining process, for example, the 1999 EuropeanCross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004Java 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 inpredictive analytics—the key standard is thePredictive Model Markup Language (PMML), which is anXML-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.[27]
Data mining is used wherever there is digital data available. Notableexamples of data mining can be found throughout business, medicine, science, finance, construction, and surveillance.
While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation touser behavior (ethical and otherwise).[28]
Data mining requires data preparation which uncovers information or patterns which compromiseconfidentiality andprivacy obligations. A common way for this to occur is throughdata 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).[32] 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.[33]
Data may also be modified so as tobecome anonymous, so that individuals may not readily be identified.[32] 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 byAOL.[34]
The inadvertent revelation ofpersonally 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 ofprivacy violation, the patrons of Walgreens filed a lawsuit against the company in 2011 for sellingprescription information to data mining companies who in turn provided the datato pharmaceutical companies.[35]
Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers. However, theU.S.–E.U. Safe Harbor Principles, developed between 1998 and 2000, currently effectively expose European users to privacy exploitation by U.S. companies. As a consequence ofEdward Snowden'sglobal surveillance disclosure, there has been increased discussion to revoke this agreement, as in particular the data will be fully exposed to theNational Security Agency, and attempts to reach an agreement with the United States have failed.[36]
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.[37]
In the United States, privacy concerns have been addressed by theUS Congress via the passage of regulatory controls such as theHealth 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 inBiotech 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 approaching a level of incomprehensibility to average individuals."[38] This underscores the necessity for data anonymity in data aggregation and mining practices.
U.S. information privacy legislation such as HIPAA and theFamily 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.
Even if there is no copyright in a dataset, the European Union recognises aDatabase right, so data mining becomes subject tointellectual property owners' rights that are protected by theDatabase Directive. UnderEuropean copyrightdatabase laws, the mining of in-copyright works (such as byweb mining) without the permission of the copyright owner is permitted under Articles 3 and 4 of the 2019Directive on Copyright in the Digital Single Market. A specific TDM exception for scientific research is described in article 3, whereas a more general exception described in article 4 only applies if the copyright holder has not opted out.
TheEuropean Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe.[39] 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 andopen access publishers to leave thestakeholder dialogue in May 2013.[40]
On the recommendation of theHargreaves review, this led to the UK government to amend its copyright law in 2014 to allow content mining as alimitation and exception.[41] The UK was 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 theInformation Society Directive (2001), 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.
Since 2020 also Switzerland has been regulating data mining by allowing it in the research field under certain conditions laid down by art. 24d of the Swiss Copyright Act. This new article entered into force on 1 April 2020.[42]
US copyright law, and in particular its provision forfair use, upholds the legality of content mining in America, and other fair use countries such asIsrael,Taiwan andSouth Korea. 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 theGoogle Book settlement the presiding judge on the case ruled thatGoogle's digitization project of in-copyright books was lawful, in part because of the transformative uses that the digitization project displayed—one being text and data mining.[43]
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 theC++ language.
NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for thePython language.
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 theJava programming language.
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.
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^Lovell, Michael C. (1983). "Data Mining".The Review of Economics and Statistics.65 (1):1–12.doi:10.2307/1924403.JSTOR1924403.
^Charemza, Wojciech W.; Deadman, Derek F. (1992). "Data Mining".New Directions in Econometric Practice. Aldershot: Edward Elgar. pp. 14–31.ISBN1-85278-461-X.
^Mena, Jesús (2011).Machine Learning Forensics for Law Enforcement, Security, and Intelligence. Boca Raton, FL: CRC Press (Taylor & Francis Group).ISBN978-1-4398-6069-4.
^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. p. 48.doi:10.1145/2023598.2023605.ISBN978-1-4503-0837-3.S2CID14967969.
^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 fromthe original(PDF) on 9 April 2020. Retrieved9 April 2020.On October 6, 2015, theCJEU ... issued a decision that invalidated Safe Harbor (effective immediately), as currently implemented.
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