A consensus based approach to constrained clustering of software requirements

Managing large-scale software projects involves a number of activities such as viewpoint extraction, feature detection, and requirements management, all of which require a human analyst to perform the arduous task of organizing requirements into meaningful topics and themes. Automating these tasks through the use of data mining techniques such as clustering could potentially increase both the efficiency of performing the tasks and the reliability of the results. Unfortunately, the unique characteristics of this domain, such as high dimensional, sparse, noisy data sets, resulting from short and ambiguous expressions of need, as well as the need for the interactive engagement of stakeholders at various stages of the process, present difficult challenges for standard clustering algorithms. In this paper, we propose a semisupervised clustering framework, based on a combination of consensus-based and constrained clustering techniques, which can effectively handle these challenges. Specific...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Chuan Duan, Jane Cleland-Huang, Bamshad Mobasher
• Keywords: CIKM 2008, Constrained Clustering Techniques, Information Management, Semisupervised Clustering Framework, Standard Clustering Algorithms