Data mining

Data mining is the process of extracting and finding patterns in massive <a href="/facts/Data_set/eDvegUTK">data sets</a> involving methods at the intersection of <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>, <a href="/facts/Statistics/DNPsKGYU">statistics</a>, and <a href="/facts/Database_system/VYcpkevb">database systems</a>. Data mining is an <a href="/facts/Interdisciplinary/HP0uaNUs">interdisciplinary</a> subfield of <a href="/facts/Computer_science/lN3pEyPz">computer science</a> and <a href="/facts/Statistics/DNPsKGYU">statistics</a> with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "<a href="/facts/Knowledge_discovery/eopEv228">knowledge discovery in databases</a>" process, or KDD. Aside from the raw analysis step, it also involves database and <a href="/facts/Data_management/Cykeqxc7">data management</a> aspects, <a href="/facts/Data_pre-processing/CS2W8m5B">data pre-processing</a>, <a href="/facts/Statistical_model/jKkT5ftm">model</a> and <a href="/facts/Statistical_inference/sJLjubxm">inference</a> considerations, interestingness metrics, <a href="/facts/Computational_complexity_theory/etHuVF3U">complexity</a> considerations, post-processing of discovered structures, <a href="/facts/Data_and_information_visualization/ET1lSjFa">visualization</a>, and <a href="/facts/Online_algorithm/64TsU7Yz">online updating</a>.
The term "data mining" is a <a href="/facts/Misnomer/P4TnsX3H">misnomer</a> because the goal is the extraction of <a href="/facts/Pattern/mzD7uLQa">patterns</a> and knowledge from large amounts of data, not the <a href="/facts/Data_scraping/8zLlaECG">extraction (mining) of data itself</a>. It also is a <a href="/facts/Buzzword/umj6poF9">buzzword</a> and is frequently applied to any form of large-scale data or <a href="/facts/Data_processing/74EKCDAq">information processing</a> (<a href="/facts/Data_collection/rEGBdDzj">collection</a>, <a href="/facts/Information_extraction/DL9nMD2X">extraction</a>, <a href="/facts/Data_warehouse/we5rblq9">warehousing</a>, analysis, and statistics) as well as any application of <a href="/facts/Decision_support_system/HPeJMlRM">computer decision support systems</a>, including <a href="/facts/Artificial_intelligence/lJGauQwX">artificial intelligence</a> (e.g., machine learning) and <a href="/facts/Business_intelligence/ftThn7Wf">business intelligence</a>. Often the more general terms (large scale) <a href="/facts/Data_analysis/CNtARxLW">data analysis</a> and <a href="/facts/Analytics/JWD5D9cM">analytics</a>—or, when referring to actual methods, artificial intelligence and machine learning—are more appropriate.
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 (<a href="/facts/Cluster_analysis/HUeDnWPt">cluster analysis</a>), unusual records (<a href="/facts/Anomaly_detection/wjEGMGGz">anomaly detection</a>), and <a href="/facts/Dependency_(computer_science)/QEeSaXF3">dependencies</a> (<a href="/facts/Association_rule_mining/oX6w9Kux">association rule mining</a>, <a href="/facts/Sequential_pattern_mining/TW9SSJeo">sequential pattern mining</a>). This usually involves using database techniques such as <a href="/facts/Spatial_index/wzPAetLd">spatial indices</a>. 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 <a href="/facts/Predictive_analytics/tbqzk5qe">predictive analytics</a>. 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 <a href="/facts/Decision_support_system/HPeJMlRM">decision support system</a>. 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 between <a href="/facts/Data_analysis/CNtARxLW">data analysis</a> and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a <a href="/facts/Marketing_campaign/LttVQ66t">marketing campaign</a>, 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.
The related terms <a href="/facts/Data_dredging/X11G2jBn">data dredging</a>, data fishing, and <a href="/facts/Data_snooping/X11G2jBn">data snooping</a> 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 mining open-in-new

Data mining