期刊
INTERNATIONAL JOURNAL OF AMBIENT COMPUTING AND INTELLIGENCE
卷 7, 期 2, 页码 47-74出版社
IGI GLOBAL
DOI: 10.4018/IJACI.2016070103
关键词
Clustering; Clustering Fitness; Expectation Maximization; Gaussian Mixture Models; K-Means; Novel Hybridization; Sum of Squared Errors
Expectation Maximization (EM) is a widely employed mixture model-based data clustering algorithm and produces exceptionally good results. However, many researchers reported that the EM algorithm requires huge computational efforts than other clustering algorithms. This paper presents an algorithm for the novel hybridization of EM and K-Means techniques for achieving better clustering performance (NovHbEMKM). This algorithm first performs K-Means and then using these results it performs EM and K-Means in the alternative iterations. Along with the NovHbEMKM, experiments are carried out with the algorithms for EM, EM using the results of K-Means and Cluster package of Purdue University. Experiments are carried out with datasets from UCI ML repository and synthetic datasets. Execution time, Clustering Fitness and Sum of Squared Errors (SSE) are computed as performance criteria. In all the experiments the proposed NovHbEMKM algorithm is taking less execution time by producing results with higher clustering fitness and lesser SSE than other algorithms including the Cluster package.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据