Deviation and Association Patterns for Subgroup Mining in Temporal, Spatial, and Textual Data Bases

Abstract

Data mining is usually introduced as search for interesting patterns in data. It is often an explorative step iteratively performed within a process of knowledge discovery in data bases (KDD). A mining step typically relies on strategies for systematic search in large hypotheses spaces guided by the autonomous evaluation of statistical tests. We describe the subgroup mining approach that is based on deviation and association patterns. A typical database contains values of attributes for many objects (persons, transactions, documents). Interpretable subgroups of these objects are searched that deviate from a designated expected behavior. Many types of data analysis questions can be answered by subgroup mining with diverse specializations of general deviation and association patterns. Tests measure the statistical interestingness of subgroup deviations. After summarizing the approach by discussing the fundamental components of subgroup pattern classes concerning validation, search and interactive presentation of pattern instances, we explain how deviation patterns of subgroup mining are applied for temporal, spatial and textual databases.