Clustering for Monitoring Software Systems Maintainability Evolution
Electronic Notes in Theoretical Computer Science (ENTCS)
Using a new relational concept to improve the clustering performance of search engines
Information Processing and Management: an International Journal
Cooperative clustering for software modularization
Journal of Systems and Software
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Clustering is particularly useful in problems where there is little prior information about the data under analysis. This is usually the case when attempting to evaluate a software system's maintainability, as many dimensions must be taken into account in order to reach a conclusion. On the other hand partitional clustering algorithms suffer from being sensitive to noise and to the initial partitioning. In this paper we propose a novel partitional clustering algorithm, k-Attractors. It employs the maximal frequent itemset discovery and partitioning in order to define the number of desired clusters and the initial cluster attractors. Then it utilizes a similarity measure which is adapted to the way initial attractors are determined. We apply the k-Attractors algorithm to two custom industrial systems and we compare it with WEKA's implementation of K-Means. We present preliminary results that show our approach is better in terms of clustering accuracy and speed.