An application for collecting and mining reports referring vulnerabilities and exposures in physical systems: A comparative study of selected clustering methods

Abstract

This paper describes an application dedicated to collecting and mining reports of software safety vulnerabilities and exposures in physical systems. This work focuses on the clustering problem of such reports, which means grouping them through automated computing process. The clustering is carried out in two stages. In the first stage, potential similarities between the reports, together with the number of clusters are detected through automated text analysis. In the second stage, a hierarchical clustering is conducted to reduce the number of these clusters to provide potential number of appropriate clusters of the group of reports. The clustering of the second stage provides the user with greater flexibility in viewing individual reports. This paper focuses mainly on the first stage of described clustering method. Two selected clustering algorithms have been compared with the aim to show how to detect the most appropriate number of groups between scraped documents. The computational experiment results are presented and discussed in the experimental section of this work.