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The development of Internet resulted in an increasing number of online text re-positories. In many cases, documents are assigned to more than one class and automatic multi-label classification needs to be used. When the number of labels exceeds the number of the documents, effective label space dimension reduction may signifi-cantly improve classification accuracy, what is a major priority in the medical field. In the paper, we propose document clustering for label selection. We use semi-clustering method, by considering graph representation, where documents are represented by vertices and edge weights are calculated according to their mutual similarity. Assigning documents to semi-clusters helps in reducing number of labels, further used in multilabel classification process. The performance of the method is examined by experiments conducted on real medical datasets.

With the growing number of IT projects and their complexity their deeper analysis became necessary. For this purpose, the historical data on previously completed projects began to collect-it has enabled projects undergo a detailed analysis, which allowed the efficient implementation of projects and allowed in advance to detect the potential problems. Thus a new area of research, Mining Software Repository (MSR), was born. Research in the field of MSR brings very promising results. The increasing importance of research for gaining the tools to support the process of collecting and analyzing the data related to software development is observed. The data from version control systems, bug tracking systems and issue tracking systems is the most popular. This paper presents the existing MSR tools carries out its analysis and describes the weaknesses of each individual solutions.