Comparative Study of Fuzzy C Means and K Means Algorithm for Requirements Clustering

Abstract

The Requirement Engineering is the most important phase of the software development life cycle which is used to translate the imprecise, incomplete needs and wishes of the potential users of software into complete, precise and formal specifications. These specifications can be decomposed on application of a data mining techniques, clustering. The process of clustering the requirements allows reducing the cost of software development and maintenance. In this research two most frequently used algorithms in clustering namely k means and fuzzy c means are used. The output generated is then analyzed for evaluating the performance of the two clustering algorithms. The requirements specified by the different stakeholders of the library are used as the input. The data mining tool WEKA was used for clustering. The clustering algorithms were then analyzed for accuracy and performance. On analysis the fuzzy c means algorithm was found to be more suitable for clustering of library requirements. The results proved to be satisfactory. Keywords: Clustering, Data Mining, Fuzzy C Means, K Means, Library, Requirements