RT - Journal Article
T1 - Tabu-KM: A Hybrid Clustering Algorithm Based on Tabu Search Approach
JF - IUST
YR - 2010
JO - IUST
VO - 21
IS - 2
UR - http://ijiepr.iust.ac.ir/article-1-195-en.html
SP - 71
EP - 79
K1 - Clustering problem
K1 - Hybrid algorithm
K1 - Tabu search algorithm
K1 - k-Means algorithm.
AB - The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the optimization property of tabu search and the local search capability of k-means algorithm together. The contribution of proposed algorithm is to produce tabu space for escaping from the trap of local optima and finding better solutions effectively. The Tabu-KM algorithm is tested on several simulated and standard datasets and its performance is compared with k-means, simulated annealing, tabu search, genetic algorithm, and ant colony optimization algorithms. The experimental results on simulated and standard test problems denote the robustness and efficiency of the algorithm and confirm that the proposed method is a suitable choice for solving data clustering problems.
LA eng
UL http://ijiepr.iust.ac.ir/article-1-195-en.html
M3
ER -