BMS: An improved Dunn index for Document Clustering validation. Issue 20 (18th October 2019)

• Record Type: Journal Article
• Authors: Misuraca, Michelangelo; Spano, Maria; Balbi, Simona
• Is Part Of: Communications in statistics. Volume 48:Issue 20(2019)
• Page Start: 5036
• Page End: 5049
• Publication Date: 2019-10-18
• Subjects: K-means -- cluster validation -- cosine similarity; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2018.1504968
• Languages: English
• ISSNs: 0361-0926

Abstract

Abstract: Document Clustering aims at organizing a large quantity of unlabeled documents into a smaller number of meaningful and coherent clusters. One of the main unsolved problems in the literature is the lack of a reliable methodology to evaluate the results, although a wide variety of validation measures has been proposed. Validation measures are often unsatisfactory with numerical data, and even underperforming with textual data. Our attention focuses on the use of cosine similarity into the clustering process. A new measure based on the same criterion is here proposed. The effectiveness of the proposal is shown by an extensive comparative study.