A New Improved K-Means Algorithm with Penalized Term

K-means algorithm is a popular method in cluster analysis. After reviewing different K-means algorithms, we propose the new penalized K-means algorithm. Originally inspired by the maximum likelihood (ML) method, a prior probability distribution assumed by classic K-means algorithm about the clustering data set was discovered, and then the new objective function for the penalized K-means algorithm was introduced. By minimizing this function with genetic algorithm, results show that this method is better than K-means algorithm in some perspectives.