文章基本信息

标题：A GENETIC ALGORITHM FOR OPTIMIZED INITIAL CENTERS K-MEANS CLUSTERING IN SMEs
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作者：BAIN KHUSUL KHOTIMAH ; FIRLI IRHAMNI ; TRI SUNDARWATI 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：90
期号：1
出版社：Journal of Theoretical and Applied
摘要：This research applies Genetic Algorithm to find the initial cluster centers and the centers of this cluster will be used as an input for the K-Means method. This method yield a more optimal performance compared to the conventional K-Means method since the centers point is optimized with Genetic Algorithm. Unfortunately, k-means is extremely sensitive to the initial choice of centers, and a poor choice of centers may lead to a local optimum that is quite inferior to the global optimum. The proposed research initial clustering and assigns at least one seed point to each cluster. During the second step, the seed-points are adjusted to minimize the cost-function. The algorithm automatically penalizes any possible winning chances for all rival seed-points in subsequent iterations base on cost-function to reaches global minimum. This method will perform optimization centers point K-means clustering using Genetic Algorithms. The Genetic Algorithms optimize centers point of the cluster more faster performance. The simulated experiments described in this paper confirm good performance have used distance measure. So, the analyses performance cluster used SSE (Sum of Squared Error). The minimum Cluster SSE of combination Genetic Algorithm with K-Means Clustering (GA+KMeans) the smallest in compared with the K-Means algorithm of Clustering SMEs.
关键词：Clustering; K-Means; Genetic Algorithm; Initial Centers; Smes