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A Multi-Objective Genetic Algorithm with Fuzzy Relational Clustering for Automatic Data Clustering

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dc.contributor.advisor Shill, Dr. Pintu Chandra.
dc.contributor.author Kundu, Animesh
dc.date.accessioned 2018-08-13T07:04:46Z
dc.date.available 2018-08-13T07:04:46Z
dc.date.copyright 2016
dc.date.issued 2016-12
dc.identifier.other ID 1207505
dc.identifier.uri http://hdl.handle.net/20.500.12228/357
dc.description This thesis is submitted to the Department of Computer Science and Engineering , Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, December 2016. en_US
dc.description Cataloged from PDF Version of Thesis.
dc.description Includes bibliographical references (pages 60-65).
dc.description.abstract A Fuzzy relational clustering algorithm (FRC) based on multi-objective nondominated Sorting genetic algorithm (NSGA-II) called FRC-NSGA-II is proposed for automatic data clustering. A given data set is spontaneously promoted into an optimal number of groups in a precise fuzzy partition through the fuzzy relational clustering algorithm, FRC. FRC operates on a similarity square matrix which is generated by comparing the pair wise similarities between data points. Multi objective NSGA-II is employed to search for appropriate number of partitions for different cluster shapes. Moreover, two well-known cluster validity indices, compactness and separation, are optimized concurrently through multi-objective NSGA-II where compactness indicates variation between data within a cluster and separation means quantifying the separation between different clusters. Real encoding schema is used for variable length NSGA-II chromosomes representing the variable number of clusters. The simulation result on benchmark data sets exhibits that the proposed method gives promising results in the complex, overlapped, high-dimensional non-gene and gene expression data sets and it has better capability of determining well-separated, hyper spherical and overlapping clusters compared with other existing clustering algorithms. en_US
dc.description.statementofresponsibility Animesh Kundu
dc.format.extent 65 pages
dc.language.iso en_US en_US
dc.publisher Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh. en_US
dc.rights without written permission. Khulna University of Engineering & Technology (KUET) thesis/ dissertation/internship reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited.
dc.subject Genetic Algorithm en_US
dc.subject Automatic Data Clustering en_US
dc.subject Fuzzy Relational Clustering en_US
dc.title A Multi-Objective Genetic Algorithm with Fuzzy Relational Clustering for Automatic Data Clustering en_US
dc.type Thesis en_US
dc.description.degree Master of Science in Computer Science and Engineering
dc.contributor.department Department of Computer Science and Engineering


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