A Feature Vector Based Approach for Software Component Clustering and Reuse Using K-means

Abstract

Software component clustering is an unsupervised learning approach which is used to cluster the software components. These clusters may then be used to study, analyze, understand behavior of the software components. In this paper, we use the k-means clustering algorithm to cluster the software components. The main difference lies in the use of distance measure which is designed to find the similarity between the software components. We use the distance measure [12], to find the pair wise project distance matrix and apply the k-means algorithm on this distance matrix. The main idea is to use more than one distance measure, to explore consensus based technique, so as to cluster software components, instead of using only one measure to cluster the components. This approach may also be applied for software architecture recovery problem by using our distance measure.