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H. Li, K. Zhang and T. Jiang, “Minimum Entropy Clustering and Applications to Gene Expression Analysis,” Proceedings of IEEE Computational System Bioinformatics Conference, Stanford, August 2004, pp. 142-151.

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• TITLE: ML-CLUBAS: A Multi Label Bug Classification Algorithm

  AUTHORS: Naresh Kumar Nagwani, Shrish Verma

  KEYWORDS: Software Bug Mining; Software Bug Classification; Bug Clustering; Classification Using Clustering; Bug Attribute Similarity; Multi Label Classification

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.12, December 31, 2012

  ABSTRACT: In this paper, a multi label variant of CLUBAS [1] algorithm, ML-CLUBAS (Multi Label-Classification of software Bugs Using Bug Attribute Similarity) is presented. CLUBAS is a hybrid algorithm, and is designed by using text clustering, frequent term calculations and taxonomic terms mapping techniques, and is an example of classification using clustering technique. CLUBAS is a single label algorithm, where one bug cluster is exactly mapped to a single bug category. However a bug cluster can be mapped into the more than one bug category in case of cluster label matches with the more than one category term, for this purpose ML-CLUBAS a multi label variant of CLUBAS is presented in this work. The designed algorithm is evaluated using the performance parameters F-measures and accuracy, number of clusters and purity. These parameters are compared with the CLUBAS and other multi label text clustering algorithms.