PSO Algorithm for Text Clustering Based on Latent Semantic Indexing

In this paper we develop a PSO algorithm based on latent semantic indexing (PSO+LSI) for text clustering. Main problem of text clustering algorithm is very high dimension because in vector space model (VSM) each term represent one dimension. Latent semantic indexing (LSI) is a technique that can reduce high dimension textual data. PSO family of bio-inspired algorithms has recently successfully been applied to a number of real word clustering problems. We use a adaptive inertia weight (AIW) that do proper exploration and exploitation in search space. PSO can merge with LSI to achieve best clustering accuracy and efficiency. the superiority of PSO+LSI over PSO+Kmeans clustering algorithm is demonstrated in two dataset (Hamshahri & Reuters).

Keywords:

Vector Space Model , PSO Algorithm , Latent Semantic Indexing , Text Clustering , Adaptive Inertia Weight ,