计算机工程2011，Vol.37Issue(15)：40-42,45,4.DOI:10.3969/j.issn.1000-3428.2011.15.011
基于竞争学习的K质心组合聚类算法
Combination Clustering Algorithm of K-Centroid Based on Competitive Learning
摘要
Abstract
For the choice of initial cluster K-Means clustering algorithm is very sensitive, its results are frequently a local optimal solution rather than a global optimal solution. On the premise of studying K-Means clustering algorithm, this paper introduces a concept of clustering and competitive learning, and proposes a combination clustering algorithm of K-Centroid based on competitive learning(CLK-Centroid algorithm). CLK-Centroid algorithm adapts the noise data and outliers by using a strategy of competitive learning to compute a cluster Centroid, and improves the precision of cluster by using a strategy of combination clustering. Building a multiple of CLK-Centroid clustering models to cluster on data set, the different sub-cluster that comes from the different clustering results must contain an intersection. The sub-cluster similarity matrix is built to merge similar cluster according to the similarity between the sub-clusters. Theoretical analysis and experimental results show that the algorithm can improve the clustering quality.关键词
CLK-Centroid算法/K-Means算法/竞争学习/组合/聚类Key words
CLK-Centroid algorithm/K-Means algorithm/competitive learning/combination/clustering分类
信息技术与安全科学引用本文复制引用
张宇,邵良衫,邱云飞,刘威..基于竞争学习的K质心组合聚类算法[J].计算机工程,2011,37(15):40-42,45,4.基金项目
国家自然科学基金资助项目(70971059) （70971059）
