9786200300621 - Optimized Data Clustering Technique for Categorical Data: an Efficient Approach by Chaudhari, Dr. Pallavi; Chaudhari, Abha (6 results)
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Taschenbuch. Condition: Neu. Optimized Data Clustering Technique for Categorical Data | An Efficient Approach | Pallavi Chaudhari (u. a.) | Taschenbuch | 132 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200300621 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrü…ck, mail[at]preigu[dot]de | Anbieter: preigu.
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Language: English
Published by LAP LAMBERT Academic Publishing Sep 2019, 2019
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 p…roposed 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. 132 pp. Englisch.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chaudhari Dr. PallaviDr. 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 expe.
Language: English
Published by LAP LAMBERT Academic Publishing Sep 2019, 2019
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 propo…sed 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 propos…ed 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.
