文章基本信息

标题：Cluster Validity Indices for Gene Expression Data
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作者：Sitansu Mohanty ; Kaberi Das ; Debahuti Mishra 等
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2013
卷号：4
期号：5-5
出版社：Seventh Sense Research Group
摘要：Clustering is mostly used to discover patterns and groups in data but there is a need to validate the quality of cluster obtained by different clustering techniques. The importance of validation arises from the fundamental definition of unsupervised learning, as clustering. So prediction of correct number of clusters is a hurdle which can be solved by using cluster validity indices that assess the validity of clusters. The hierarchical clustering is one of the most ongoing clustering techniques to have the different clusters as gene subset that is calculated by measuring the intra as well as inter distance of each data point. The kmeans clustering is another effective technique which is based on the number of clusters. Our research has been focused on evaluating the valid clusters through different validity indices. In this paper, we have applied two validity indices that is Silhouette index and Dunn’s index and a comparison has been made upon the resultant of each clustering technique.
关键词：Clustering; k-means clustering; hierarchical clustering; cluster validity indices