An incremental approach to semantic clustering designed for software visualization

Abstract

In this paper, we introduce an incremental approach to semantic clustering, designed for software visualization, inspired by behavior of fire ant colony. Our technique focus on identification of equally sized but natural clusters that provides better hindsight of software system structure for development participants. We also address performance issues of existing approaches by maintaining similarities based on global weights incrementally, using subspaces and covariance matrix. Effectivity of visualization is improved by representing multiple documents with precise medoid approximation.