RESEARCH ON SELECTING INITIAL POINTS FOR K-MEANS CLUSTERING

摘要：

Clustering analysis is one of the important problems in the fields of data mining and machine learning. There are many different clustering methods. Among them, k-means clustering is one of the most popular schemes owing to its simple and practicality. This paper investigates the approximate algorithm for the A-means clustering by means of selecting the k initial points from the input point set. An expected 2-approximation algorithm is presented in this paper. Meanwhile, an efficient algorithm for selecting the initial points is also proposed. At last some experimental results are given to test the valid of these algorithms.

关键词： Clustering A-means clustering Randomized algorithm

作者: SHOU-QIANG WANG DA-MING ZHU

作者单位: School of Computer Science and Technology, Shandong University, Jinan 250100,China Department of Inf School of Computer Science and Technology, Shandong University, Jinan 250100,China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 2673-2677

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）