Mining Structured Association Patterns from Databases

Authors:
Hirofumi Matsuzawa;Takeshi Fukuda
Affiliations:
-;-
Venue:
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Year:
2000

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Abstract

We consider the data-mining problem of discovering structured association patterns from large databases. A structured association pattern is a set of sets of items that can represent a two level structure in some specified set of target data. Although the structure is very simple, it cannot be extracted by conventional pattern discovery algorithms. We present an algorithm that discovers all frequent structured association patterns. We were motivated to consider the problem by a specific text mining application, but our method is applicable to a broad range of data mining applications. Experiments with synthetic and real data show that our algorithm efficiently discovers structured association patterns in a large volume of data.