Document Clustering with Grouping and Chaining Algorithms

Document clustering has many uses in natural language tools and applications. For instance, summarizing sets of documents that all describe the same event requires first identifying and grouping those documents talking about the same event. Document clustering involves dividing a set of documents into non-overlapping clusters. In this paper, we present two document clustering algorithms: grouping algorithm, and chaining algorithm. We compared them with k-means and the EM algorithms. The evaluation results showed that our two algorithms perform better than the k-means and EM algorithms in different experiments.

• Type: Conference
• Year: 2005
• Where: IJCNLP
• Authors: Yllias Chali, Soufiane Noureddine
• Keywords: Document Clustering, Document Clustering Algorithms, EM Algorithms, IJCNLP 2005, Natural Language Processing