K-harmonic means Data Clustering using Combination of Particle Swarm Optimization and Tabu Search
Publish place: International Journal of Mechatronics, Electrical and Computer Technology، Vol: 4، Issue: 11
Publish Year: 1393
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJMEC-4-11_002
تاریخ نمایه سازی: 16 فروردین 1395
Abstract:
Clustering is one of the widely used techniques for data analysis. Also it is a tool to discover structures from inside of data without any previous knowledge. K-harmonic means (KHM) is a center-based clustering algorithm which solves sensitivity to initialization of the centers which is the main drawback of K-means (KM) algorithm, but, both KM and KHM converge to local optimal. In this paper, a hybrid data clustering algorithm based on KHM is proposed called PSOTSKHM, using Particle Swarm Optimization (PSO) algorithm as a stochastic global optimization technique and Tabu Search (TS) algorithm as a local search method. This algorithm makes full use of the advantages of three algorithms. The proposed algorithm has been compared with KHM, PSOKHM and IGSAKHM algorithms on four real datasets and the obtained results show the superiority of suggested algorithm in most cases.
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Authors
Tahereh Aghdasi
Islamic Azad University, Science and Research Branch of Ayatollah Amoli, Amol, Iran
Javad Vahidi
Iran University of Science and Technology, Tehran, Iran
Homayoon Motameni
Islamic Azad University, Sari, Iran
Mohammad Madadpour Inallou
Young Researchers and Elites Club, Islamic Azad University, West Tehran Branch, Tehran, Iran