基于最远点启发式的K-Modes聚类算法
Farthest-Point Heuristic based Initialization Methods for K-Modes Clustering
摘要：
k-modes算法已经成为解决不同应用领域中分类数据聚类问题的一种流行技术。然而，该算法需要随机选择聚类的初始点。不同的初始点往往会导致相当不同的聚类结果。在本文中，我们提出了一个实验研究应用最远点启发式的初始化方法的k模式聚类，以提高其性能。实验结果表明，新的初始化方法导致更好的聚类精度比随机选择初始化方法的k-模式聚类。
英文摘要：
The k-modes algorithm has become a popular technique in solving categorical data clustering problems in different application domains. However, the algorithm requires random selection of initial points for the clusters. Different initial points often lead to considerable distinct clustering results. In this paper we present an experimental study on applying a farthest-point heuristic based initialization method to k-modes clustering to improve its performance. Experiments show that new initialization method leads to better clustering accuracy than random selection initialization method for k-modes clustering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
