An Improved Self-Organizing Map for Bugs Data Clustering

In software projects, there is a data repository which contains the bug reports. These bugs are required to carefully analyse and resolve the problem. Handling these bugs humanly is extremely time consuming process, and it can result the delaying in addressing some important bugs resolutions. To overcome this problem, researchers have introduced many techniques. One of the commonly used algorithm is K-means, which is considered as the simplest supervised learning algorithm for clustering, yet it tends to produce smaller number of clusters, while considering the unsupervised learning algorithms, Self-Organizing Map (SOM) considers the equally compatible algorithm for clustering, as both the algorithms are closely related but differently used in data mining. This paper attempts to provide a comparative analysis of both the clustering algorithms and for attaining the results, a series of experiment has been conducted using Mozilla bugs data set. Based on the results, this paper proposes a new algorithm which is improved SOM using Jaccard New Measure. The test result has proved that the proposed new method produced better accuracy.