Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Knowledge discovery in databases terminology
Advances in knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Robust discovery of local patterns: subsets and stratification in adverse drug reaction surveillance
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
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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.