Optimized Data Clustering Technique for Categorical Data - Chaudhari, Pallavi;Chaudhari, Abha
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In this book, a new clustering technique for categorical-data is introduced. Essentially, the effectiveness of a clustering technique is significantly determined by two aspects, the searching method and the proximity criteria. The proposed algorithm uses a genetic algorithm for clustering that is shown in the experiments to be an efficient clustering method for categorical-data. The proximity criteria adopt a rule-based information theoretical measure called weight of evidence. It finds the interesting patterns and measures the weight of these patterns that supporting the presence of an…mehr

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Produktbeschreibung
In this book, a new clustering technique for categorical-data is introduced. Essentially, the effectiveness of a clustering technique is significantly determined by two aspects, the searching method and the proximity criteria. The proposed algorithm uses a genetic algorithm for clustering that is shown in the experiments to be an efficient clustering method for categorical-data. The proximity criteria adopt a rule-based information theoretical measure called weight of evidence. It finds the interesting patterns and measures the weight of these patterns that supporting the presence of an objective-value pair to be relevant to a cluster label. By summing up the total weight that the records acquire in the patterns due to presence of both the objective-value and the corresponding cluster label, the fitness in the chromosome is measured and hence how best the records are clustered together is seen.
Autorenporträt
Dr. Pallavi Chaudhari is Associate Professor and Head of Department of Information Technology (IT), Priyadarshini Institute of Engineering and Technology (PIET), Nagpur. She has 20 years of teaching experience. Dr. Pallavi Chaudhar