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BMS: An improved Dunn index for Document Clustering validation

Author

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  • Michelangelo Misuraca
  • Maria Spano
  • Simona Balbi

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.

Suggested Citation

  • Michelangelo Misuraca & Maria Spano & Simona Balbi, 2019. "BMS: An improved Dunn index for Document Clustering validation," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(20), pages 5036-5049, October.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:20:p:5036-5049
    DOI: 10.1080/03610926.2018.1504968
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