Abstract
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.
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Zhang, Y., Cheng, E. (2013). An Optimized Method for Selection of the Initial Centers of K-Means Clustering. In: Qin, Z., Huynh, VN. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2013. Lecture Notes in Computer Science(), vol 8032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39515-4_13
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DOI: https://doi.org/10.1007/978-3-642-39515-4_13
Publisher Name: Springer, Berlin, Heidelberg
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