Editing Data mining (section)

===Data mining===
Data mining involves six common classes of tasks:<ref name="Fayyad">{{cite web |last1=Fayyad |first1=Usama |author-link1=Usama Fayyad |last2=Piatetsky-Shapiro |first2=Gregory|author-link2=Gregory Piatetsky-Shapiro |last3=Smyth |first3=Padhraic |title=From Data Mining to Knowledge Discovery in Databases |year=1996 |url=http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf |archive-url=https://ghostarchive.org/archive/20221009/http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf |archive-date=2022-10-09 |url-status=live |access-date = 17 December 2008 }}</ref>

* [[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.
* [[Cluster 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.
* [[Statistical classification|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 analysis|Regression]] – attempts to find a function that models the data with the least error that is, for estimating the relationships among data or datasets.
* [[Automatic summarization|Summarization]] – providing a more compact representation of the data set, including visualization and report generation.