A heuristic algorithm for solving the minimum sum-of-squares clustering problems

作者：Ordin Burak^*; Bagirov Adil M

来源：Journal of Global Optimization, 2015, 61(2): 341-361.

DOI：10.1007/s10898-014-0171-5

• Minimum sum-of-squares clustering
• Global optimization
• Nonsmooth optimization
• k-means algorithm
• Global k-means algorithm

Clustering is an important task in data mining. It can be formulated as a global optimization problem which is challenging for existing global optimization techniques even in medium size data sets. Various heuristics were developed to solve the clustering problem. The global -means and modified global -means are among most efficient heuristics for solving the minimum sum-of-squares clustering problem. However, these algorithms are not always accurate in finding global or near global solutions to the clustering problem. In this paper, we introduce a new algorithm to improve the accuracy of the modified global -means algorithm in finding global solutions. We use an auxiliary cluster problem to generate a set of initial points and apply the -means algorithm starting from these points to find the global solution to the clustering problems. Numerical results on 16 real-world data sets clearly demonstrate the superiority of the proposed algorithm over the global and modified global -means algorithms in finding global solutions to clustering problems.

• 出版日期2015-2