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Data Clustering Using Hybrid Genetic Algorithm with k-Means and k-Medoids Algorithms

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dc.contributor.author Islam, Md. Touhidul
dc.contributor.author Basak, Pappu Kumar
dc.contributor.author Bhowmik, Priom
dc.date.accessioned 2022-09-06T03:49:19Z
dc.date.available 2022-09-06T03:49:19Z
dc.date.issued 2019-09-26
dc.identifier.uri http://dspace.ewubd.edu:8080/handle/123456789/3704
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh. en_US
dc.description.abstract Clustering methods separate a set of data points into groups or clusters, where data points of each cluster have the similar properties and are dissimilar from those of other clusters. In general k-means and k-medoids methods are used for data clustering. These clustering methods are heuristic and may stuck in a local optimum. To avoid this problem, we propose a hybrid Genetic Algorithm (HGA) for data clustering. For this purpose, we propose a genetic encoding of the clustering problem, where data points are separated into k clusters. The cluster centers of the generated clusters are determined using the techniques of both k-means and k-medoids methods. The fitness of the clustering is calculated using the sum of Euclidian distances of each data point from its cluster center. We experiment with Iris, Seeds, and Ionosphere datasets. Experimental results show that the proposed HGA generates 2.67% to 28.68% higher clustering accuracies than the clustering accuracies previously reported in the literature. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;CSE00206
dc.subject Data Clustering, Hybrid Genetic Algorithm, k-Means and k-Medoids Algorithms en_US
dc.title Data Clustering Using Hybrid Genetic Algorithm with k-Means and k-Medoids Algorithms en_US
dc.type Thesis en_US


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