Authors: Hansen, Pierre
Mladenović, Nenad 
Title: J-Means: a new local search heuristic for minimum sum of squares clustering
Journal: Pattern Recognition
Volume: 34
Issue: 2
First page: 405
Last page: 413
Issue Date: 1-Jan-2001
Rank: M21
ISSN: 0031-3203
DOI: 10.1016/S0031-3203(99)00216-2
Abstract: 
A new local search heuristic, called J-Means, is proposed for solving the minimum sum of squares clustering problem. The neighborhood of the current solution is defined by all possible centroid-to-entity relocations followed by corresponding changes of assignments. Moves are made in such neighborhoods until a local optimum is reached. The new heuristic is compared with two other well-known local search heuristics, K- and H-Means as well as with H-Means +, an improved version of the latter in which degeneracy is removed. Moreover, another heuristic, which fits into the variable neighborhood search metaheuristic framework and uses J-Means in its local search step, is proposed too. Results on standard test problems from the literature are reported. It appears that J-Means outperforms the other local search methods, quite substantially when many entities and clusters are considered.
Publisher: Elsevier

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