An overview of data warehousing and OLAP technology
ACM SIGMOD Record
A statistical perspective on knowledge discovery in databases
Advances in knowledge discovery and data mining
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Towards on-line analytical mining in large databases
ACM SIGMOD Record
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Complex Aggregation at Multiple Granularities
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
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Descriptive data mining is the description of a set of data in a concise and summary manner and the presentation of the general properties of the data. Mining characteristic rules and discriminant rules from the data are two essential components in descriptive data mining. In contrast to online analytical processing, data description puts more emphasis on (1) automated processing, helping users determine which dimensions (or attributes) should be included in the analysis and to what abstraction level the data set should be generalized in order to obtain interesting summarization; and (2) handling complex data types. Mining data characteristics and discriminant descriptions can be implemented based on a data cube method or an attribute-oriented induction method. Moreover, data description can be enhanced by data dispersion analysis, multifeature data cubes, and discovery-driven data cubes.