Hegland, Markus Data mining techniques. (English) Zbl 1123.68343 Acta Numerica 10, 313-355 (2001). Summary: Methods for knowledge discovery in data bases (KDD) have been studied for more than a decade. New methods are required owing to the size and complexity of data collections in administration, business and science. They include procedures for data query and extraction, for data cleaning, data analysis, and methods of knowledge representation. The part of KDD dealing with the analysis of the data has been termed data mining. Common data mining tasks include the induction of association rules, the discovery of functional relationships (classification and regression) and the exploration of groups of similar data objects in clustering. This review provides a discussion of and pointers to efficient algorithms for the common data mining tasks in a mathematical framework. Because of the size and complexity of the data sets, efficient algorithms and often crude approximations play an important role. Cited in 1 ReviewCited in 1 Document MSC: 68T05 Learning and adaptive systems in artificial intelligence 68P15 Database theory 68T30 Knowledge representation PDFBibTeX XMLCite \textit{M. Hegland}, Acta Numerica 10, 313--355 (2001; Zbl 1123.68343) Full Text: DOI