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High-Order Pattern Discovery from Discrete-Valued Data
November-December 1997 (vol. 9 no. 6)
pp. 877-893

Abstract—To uncover qualitative and quantitative patterns in a data set is a challenging task for research in the area of machine learning and data analysis. Due to the complexity of real-world data, high-order (polythetic) patterns or event associations, in addition to first-order class-dependent relationships, have to be acquired. Once the patterns of different orders are found, they should be represented in a form appropriate for further analysis and interpretation. In this paper, we propose a novel method to discover qualitative and quantitative patterns (or event associations) inherent in a data set. It uses the adjusted residual analysis in statistics to test the significance of the occurrence of a pattern candidate against its expectation. To avoid exhaustive search of all possible combinations of primary events, techniques of eliminating the impossible pattern candidates are developed. The detected patterns of different orders are then represented in an attributed hypergraph which is lucid for pattern interpretation and analysis. Test results on artificial and real-world data are discussed toward the end of the paper.

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Index Terms:
Adjusted residual, attributed hypergraph, data analysis, database mining, machine learning, pattern discovery, pattern representation.
Citation:
Andrew K.C. Wong, Yang Wang, "High-Order Pattern Discovery from Discrete-Valued Data," IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 6, pp. 877-893, Nov.-Dec. 1997, doi:10.1109/69.649314
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