Synthesizing knowledge: A cluster analysis approach using event covering
IEEE Transactions on Systems, Man and Cybernetics
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Visualizing processes in neural networks
IBM Journal of Research and Development
Approximation and radial-basis-function networks
Neural Computation
Self-organizing maps
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
High-Order Pattern Discovery from Discrete-Valued Data
IEEE Transactions on Knowledge and Data Engineering
On maximum entropy discretization and its applications in pattern recognition
On maximum entropy discretization and its applications in pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Marginal Maximum Entropy Partitioning Yields Asymptotically Consistent Probability Density Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Formal logics of discovery and hypothesis formation by machine
Theoretical Computer Science
Symbolic time series analysis via wavelet-based partitioning
Signal Processing - Special section: Distributed source coding
Pattern identification in dynamical systems via symbolic time series analysis
Pattern Recognition
Pattern discovery for large mixed-mode database
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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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.