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A Framework for Statistical Data Mining with Summary Tables
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W.-C. Hou
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Return to Paper Session 1: Scientific and Statistical Data Warehouses
In this paper, we present a framework for statistical data mining using summary tables. A set of operators is proposed for common data mining tasks, such as summarization, association, classification, and clustering, as well as for basic statistical analysis, such as hypothesis testing, estimation, and regression, which can help explore knowledge. The operators enable users to explore a variety of knowledge effectively and yet require users little statistical knowledge.
Summary tables, which store basic information about groups of tuples of the underlying relations, are constructed to speed up the data mining process. The summary tables are incrementally updateable and are able to support a variety of data mining and statistical analysis tasks. The operators together with the uses of summary tables can make interactive data mining flexible, effective, and perhaps instantaneous.
Copyright(C) 2000 ACM
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