Study of a Reasonable Initial Center Selection Method Applied to a K-Means Clustering

作者：Lee WonHee; Lee Samuel Sangkon; An Dong Un^*

来源：IEICE Transactions on Information and Systems, 2013, E96D(8): 1727-1733.

DOI：10.1587/transinf.E96.D.1727

• initial center selection
• max average distance
• time complexity
• K-means algorithm
• document clustering

Clustering methods are divided into hierarchical clustering, partitioning clustering, and more. K-Means is a method of partitioning clustering. We improve the performance of a K-Means, selecting the initial centers of a cluster through a calculation rather than using random. selecting. This method maximizes the distance among the initial centers of clusters. Subsequently, the centers are distributed evenly and the results are more accurate than for initial cluster centers selected at random. This is time-consuming, but it can reduce the total clustering time by minimizing allocation and recalculation. Compared with the standard algorithm, F-Measure is more accurate by 5.1%.

• 出版日期2013-8