Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simple and fast algorithm for K-medoids clustering
Expert Systems with Applications: An International Journal
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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At present, K-Means algorithm as a method of clustering based on partition has more applications. By analyzing the problem of K-Means, we propose a kind of optimized algorithm to select initial clustering points, which utilize an adjacent similar degree between points to get similarity density, then by means of max density points selecting our method heuristically generate clustering initial centers to get more reasonable clustering results. This method compares to traditional methods lie in it will get suitable initial clustering centers and also has more stable clustering result. Finally, through comparative experiments we prove the effectiveness and feasibility of this algorithm.