期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2015
卷号：4
期号：9
页码：14192-14196
DOI：10.18535/ijecs/v4i9.22
出版社：IJECS
摘要：Clustering is one of the prominent fields of data mining. A major drawback of traditional clustering algorithms is that theyperform clustering on static databases. But in real time databases are dynamic. Therefore incremental clustering algorithms have become aninteresting area of research wherein clustering is performed on the incremental data without having to cluster the entire data from scrape. Inthis paper, a new incremental clustering algorithm called Incremental Shared Nearest Neighbor Clustering Approach (ISNNCA) fornumeric data has been proposed. This algorithm performs clustering based on a similarity measure which is obtained from the number ofnearest neighbors that two points share. In order to identify nearest neighbors, a distance measure is used. A distance measure that performswell with this algorithm has been identified in this work. This algorithm could find clusters of different shapes, sizes and densities
