OPTIMIZATION OF K-MEANS CLUSTERING USING GENETIC ALGORITHM

Gaurav Dwivedi

ISBN 10: 6206737047 / ISBN 13: 9786206737049

Published by LAP LAMBERT Academic Publishing Jul 2023, 2023

About this Item

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.

Seller Inventory # 9786206737049

Bibliographic Details

• Title: OPTIMIZATION OF K-MEANS CLUSTERING USING GENETIC ALGORITHM
• Publisher: LAP LAMBERT Academic Publishing Jul 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 6206737047
• ISBN 13: 9786206737049
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.