A Novel k'-Means Algorithm for Clustering Analysis

Chonglun Fang, Jinwen Ma
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引用次数: 9

Abstract

This paper proposes a novel k-means algorithm for clustering analysis for the cases that the true number of clusters in a data or points set is not known in advance. That is, assuming that the number of seed-points in the algorithm is set to be larger than the true number k of clusters in the data set, the proposed algorithm can assign the k seed-points to the actual clusters, respectively, with the extra seed-points corresponding to the empty clusters, i.e., having no winning points according to a newly defined distance. Via using the Mahalanobis distance, the proposed algorithm can be further extended to elliptical clustering analysis. It is demonstrated well by the experiments on simulated data set and the wine data that the proposed k- means algorithm can find the correct number of clusters in the sample data with a good correct classification rate. Moreover, the algorithm is successfully applied to unsupervised color image segmentation.
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一种新的k′-均值聚类分析算法
本文提出了一种新的k-means聚类分析算法,用于事先不知道数据或点集中的真实聚类数量的情况。即假设算法中种子点的个数大于数据集中簇的真实个数k,则算法可以将k个种子点分别分配给实际簇,多余的种子点对应于空簇,即根据新定义的距离没有获胜点。通过利用马氏距离,该算法可以进一步扩展到椭圆聚类分析。在模拟数据集和葡萄酒数据上的实验表明,所提出的k- means算法能够在样本数据中找到正确的簇数,并具有良好的正确分类率。此外,该算法还成功地应用于无监督彩色图像分割。
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