Paper Info
Title
High-Order Pattern Discovery from Discrete-Valued Data
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.
Year
DOI
Venue
1997
10.1109/69.649314
IEEE Trans. Knowl. Data Eng.
Keywords
Field
DocType
pattern interpretation,adjusted residual analysis,real-world data,data analysis,quantitative pattern,high-order pattern discovery,event association,different order,impossible pattern candidate,discrete-valued data,pattern candidate,exhaustive search,first order,pattern recognition,machine learning,learning artificial intelligence
Graph theory,Data mining,Residual,Search algorithm,Brute-force search,Pattern recognition,Computer science,Hypergraph,Artificial intelligence,Machine learning,Computational complexity theory
Journal
Volume
Issue
ISSN
9
6
1041-4347
Citations 
PageRank 
References 
52
121.27
23
Authors
2
Name
Order
Citations
PageRank
Andrew K. C. Wong14063518.39
Yang Wang2948155.42