J-means and I-means for minimum sum-of-squares clustering on networks
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- 作者：Alexey Nikolaev ; Nenad Mladenović ; Raca Todosijević
- 关键词：Minimum sum ; of ; squares clustering ; K ; means ; J ; means ; Heuristic ; Variable neighborhood search
- 刊名：Optimization Letters
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：11
- 期：2
- 页码：359-376
- 全文大小：
- 刊物类别：Mathematics and Statistics
- 刊物主题：Optimization; Operation Research/Decision Theory; Computational Intelligence; Numerical and Computational Physics, Simulation;
- 出版者：Springer Berlin Heidelberg
- ISSN：1862-4480
- 卷排序：11
文摘
Given a graph, the Edge minimum sum-of-squares clustering problem requires finding p prototypes (cluster centres) by minimizing the sum of their squared distances from a set of vertices to their nearest prototype, where a prototype can be either a vertex or an inner point of an edge. In this paper we have implemented Variable neighborhood search based heuristic for solving it. We consider three different local search procedures, K-means, J-means, and a new I-means heuristic. Experimental results indicate that the implemented VNS-based heuristic produces the best known results in the literature.