Online from: 1972
Subject Area: Electrical & Electronic Engineering
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|Title:||Ant colony based hybrid optimization for data clustering|
|Author(s):||Amarendra Nath Sinha, (Department of Mechanical Engineering, Birla Institute of Technology, Mesra Deemed University, Ranchi, India), Nibedita Das, (Department of Computer Science and Engineering, Birla Institute of Technology, Mesra Deemed University, Ranchi, India), Gadadhar Sahoo, (Department of Computer Science and Engineering, Birla Institute of Technology, Mesra Deemed University, Ranchi, India)|
|Citation:||Amarendra Nath Sinha, Nibedita Das, Gadadhar Sahoo, (2007) "Ant colony based hybrid optimization for data clustering", Kybernetes, Vol. 36 Iss: 2, pp.175 - 191|
|Keywords:||Cybernetics, Data collection, Optimization techniques, Simulation|
|Article type:||Research paper|
|DOI:||10.1108/03684920710741215 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
Purpose – A new algorithm based on ant colony optimization (ACO) for data clustering has been developed.
Design/methodology/approach – ACO technique along with simulated annealing, tournament selection (GA), Tabu search and density distribution are used to solve unsupervised clustering problem for making similar groups from arbitrarily entered large data.
Findings – Distinctive clusters of similar data are formed metaheuritically from arbitrarily entered mixed data based on similar attributes of data.
Research limitations/implications – The authors have run a computer program for a number of cases related to data clustering. So far, there are no problems in convergence of results for formation of distinctive similar groups with given data set quickly and accurately.
Practical implications – ACO-based method developed here can be applied to practical industrial problems for mobile robotic navigation other than data clustering and travelling salesman.
Originality/value – This paper will enable the solving of problems related to mixed data, which requires the formation of a number of groups of similar data without having a prior knowledge of divisions, which lead to unbiased clustering. The computer code developed in this work is based on a metaheuristic algorithm and presented here to solve a number of cases.
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