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Over the course of time, a tremendous amount of data is accumulated. Information extraction is one of the most time-consuming processes because it varies greatly depending on the user's requirements. Data mining's varied approaches are employed to compile relevant data and present it in a digestible fashion for end users. Clustering and classification are two data mining techniques used to uncover previously unseen patterns and insights.This summary discusses the use of data mining techniques, specifically clustering and classification, to extract relevant information from accumulated data. It highlights the importance of selecting a suitable clustering algorithm and introduces the concept of using a genetic algorithm to improve the k-means clustering method. The proposed method aims to optimize the clustering process and demonstrates its effectiveness through a scenario-based test. The summary concludes by suggesting future research to further optimize the k-means algorithm using various evolutionary methods.
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OPTIMIZATION OF K-MEANS CLUSTERING USING GENETIC ALGORITHM
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LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206737047
ISBN 13: 9786206737049
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OPTIMIZATION OF K-MEANS CLUSTERING USING GENETIC ALGORITHM
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LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206737047
ISBN 13: 9786206737049
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OPTIMIZATION OF K-MEANS CLUSTERING USING GENETIC ALGORITHM
Verlag:
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206737047
ISBN 13: 9786206737049
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OPTIMIZATION OF K-MEANS CLUSTERING USING GENETIC ALGORITHM
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LAP LAMBERT Academic Publishing Jul 2023, 2023
ISBN 10: 6206737047
ISBN 13: 9786206737049
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Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Over the course of time, a tremendous amount of data is accumulated. Information extraction is one of the most time-consuming processes because it varies greatly depending on the user's requirements. Data mining's varied approaches are employed to compile relevant data and present it in a digestible fashion for end users. Clustering and classification are two data mining techniques used to uncover previously unseen patterns and insights.This summary discusses the use of data mining techniques, specifically clustering and classification, to extract relevant information from accumulated data. It highlights the importance of selecting a suitable clustering algorithm and introduces the concept of using a genetic algorithm to improve the k-means clustering method. The proposed method aims to optimize the clustering process and demonstrates its effectiveness through a scenario-based test. The summary concludes by suggesting future research to further optimize the k-means algorithm using various evolutionary methods. 76 pp. Englisch. Bestandsnummer des Verkäufers 9786206737049
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OPTIMIZATION OF K-MEANS CLUSTERING USING GENETIC ALGORITHM
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ISBN 10: 6206737047
ISBN 13: 9786206737049
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Over the course of time, a tremendous amount of data is accumulated. Information extraction is one of the most time-consuming processes because it varies greatly depending on the user s requirements. Data mining s varied approaches are employed to compile r. Bestandsnummer des Verkäufers 1030399590
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OPTIMIZATION OF K-MEANS CLUSTERING USING GENETIC ALGORITHM
Verlag:
LAP LAMBERT Academic Publishing Jul 2023, 2023
ISBN 10: 6206737047
ISBN 13: 9786206737049
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Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: Neu. Neuware -Over the course of time, a tremendous amount of data is accumulated. Information extraction is one of the most time-consuming processes because it varies greatly depending on the user's requirements. Data mining's varied approaches are employed to compile relevant data and present it in a digestible fashion for end users. Clustering and classification are two data mining techniques used to uncover previously unseen patterns and insights.This summary discusses the use of data mining techniques, specifically clustering and classification, to extract relevant information from accumulated data. It highlights the importance of selecting a suitable clustering algorithm and introduces the concept of using a genetic algorithm to improve the k-means clustering method. The proposed method aims to optimize the clustering process and demonstrates its effectiveness through a scenario-based test. The summary concludes by suggesting future research to further optimize the k-means algorithm using various evolutionary methods.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch. Bestandsnummer des Verkäufers 9786206737049
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OPTIMIZATION OF K-MEANS CLUSTERING USING GENETIC ALGORITHM
Verlag:
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206737047
ISBN 13: 9786206737049
Neu
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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Over the course of time, a tremendous amount of data is accumulated. Information extraction is one of the most time-consuming processes because it varies greatly depending on the user's requirements. Data mining's varied approaches are employed to compile relevant data and present it in a digestible fashion for end users. Clustering and classification are two data mining techniques used to uncover previously unseen patterns and insights.This summary discusses the use of data mining techniques, specifically clustering and classification, to extract relevant information from accumulated data. It highlights the importance of selecting a suitable clustering algorithm and introduces the concept of using a genetic algorithm to improve the k-means clustering method. The proposed method aims to optimize the clustering process and demonstrates its effectiveness through a scenario-based test. The summary concludes by suggesting future research to further optimize the k-means algorithm using various evolutionary methods. Bestandsnummer des Verkäufers 9786206737049
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