The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.06 - November/December (1999 vol.11)
pp: 833-852
ABSTRACT
<p><b>Abstract</b>—In this paper, a novel method of pattern discovery is proposed. It is based on the theoretical formulation of a contingency table of events. Using residual analysis and recursive partitioning, statistically significant events are identified in a data set. These events constitute the important information contained in the data set and are easily interpretable as simple rules, contour plots, or parallel axes plots. In addition, an informative probabilistic description of the data is automatically furnished by the discovery process. Following a theoretical formulation, experiments with real and simulated data will demonstrate the ability to discover subtle patterns amid noise, the invariance to changes of scale, cluster detection, and discovery of multidimensional patterns. It is shown that the pattern discovery method offers the advantages of easy interpretation, rapid training, and tolerance to noncentralized noise.</p>
INDEX TERMS
Pattern discovery, residual analysis, recursive partitioning, events, contingency tables.
CITATION
Tom Chau, Andrew K.C. Wong, "Pattern Discovery by Residual Analysis and Recursive Partitioning", IEEE Transactions on Knowledge & Data Engineering, vol.11, no. 6, pp. 833-852, November/December 1999, doi:10.1109/69.824592
27 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool