Global k-Means with Similarity Functions
The k-means algorithm is a frequently used algorithm for solving clustering problems. This algorithm has the disadvantage that it depends on the initial conditions, for that reason, the global k-means algorithm was proposed to solve this problem. On the other hand, the k-means algorithm only works with numerical features. This problem is solved by the k-means algorithm with similarity functions that allows working with qualitative and quantitative variables and missing data (mixed and incomplete data). However, this algorithm still depends on the initial conditions. Therefore, in this paper an algorithm to solve the dependency on initial conditions of the k-means algorithm with similarity functions is proposed, our algorithm is tested and compared against k-means algorithm with similarity functions.
KeywordsObjective Function Local Search Similarity Function Optimal Position Cluster Problem
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