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A PATTERN SEARCH IN DATA ANALYSIS

International Journal of Electronic Commerce Studies

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Title A PATTERN SEARCH IN DATA ANALYSIS
 
Creator Tzeng, Chun-Hung; Ball State University
Sun, Fu-Shing; Ball State University
 
Description This paper introduces a probabilistic model of two-class pattern recognition. The measurable sets are defined by a similarity, which is a reflexive and symmetric binary relation. The heuristic information model is formulated by a type of data clustering called representative clustering. The heuristic information about a data record is a data subset containing the record, which is computed by comparing the record with all representative records. For the corresponding classifiers, both Bayes and Neyman-Pearson Theorems are proved in this paper. In application, the knowledge discovering process searches for similarity and representative clustering in a training data set. The evaluation is extended to records in a testing data set. The experiment shows the trade-off between the number of representatives and classifier performance.
 
Publisher Academy of Taiwan Information Systems Research
 
Date 2010-11-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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Format application/pdf
 
Identifier http://academic-pub.org/ojs/index.php/ijecs/article/view/920
 
Source International Journal of Electronic Commerce Studies; Vol 1, No 2 (2010); 117-138
2073-9729
 
Language eng
 
Relation http://academic-pub.org/ojs/index.php/ijecs/article/view/920/103
 
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