Pattern discovery by residual analysis and recursive partitioning

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 inform...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 1999-11, Vol.11 (6), p.833-852
Main Authors: Chau, T., Wong, A.K.C.
Format: Article
Language:English
Subjects:
Data analysis
Demography
Economic forecasting
Formulations
Geometry
Kernel
Multidimensional systems
Noise
Partitioning
Pattern analysis
Petroleum
Radial basis function networks
Recursive
Shape
Simulation
Systems engineering and theory
Tables (data)
Tolerances
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Summary: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.
ISSN:1041-4347
1558-2191
DOI:10.1109/69.824592