Nepali Document Clustering using DBSCAN and OPTICS Algorithm

Abstract

Automated document clustering is the process of grouping documents into a small sets of meaningful collections based on similarity between them. This research evaluates density based clustering algorithms namely Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering points to Identify Cluster Structure(OPTICS) algorithms using four performance metrics: Homogeneity, Completeness, V-Measure and Silhouette Coefficient on Nepali dataset. Features extraction is done using combination of Term Frequency – Inverse Document Frequency (TFIDF) with Latent Semantic Indexing (LSI). The results based on the performance metrics mentioned above shows that clustering result of DBSCAN is slightly better than OPTICS algorithm. The time required for processing is better for DBSCAN algorithm.