期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2013
卷号：51
期号：2
出版社：Journal of Theoretical and Applied
摘要：Hierarchical clustering is the grouping of objects of interest according to their similarity into a hierarchy, with different levels reflecting the degree of inter-object resemblance. It is an important area in data analysis and pattern recognition. In this paper, the scholar proposes a new approach for robust hierarchical clustering based on the distance function between each data object and the cluster centers. This method avoids the need to compute the distance of each data object to the cluster center. It saves running time. The experimental results showed that the best clusters were obtained using EIDA method, this suggests that this similarity measure would be applicable to biological data sets.
