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A differential evolution based algorithm to cluster text corpora using lazy re-evaluation of fringe points

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Abstract

Document clustering is a well established technique used to segregate voluminous text corpora into distinct categories. In this paper we present an improved algorithm for clustering large text corpus. The proposed algorithm tries to overcome the challenges of clustering large corpora, while maintaining high ”goodness” values for the proposed clusters. The algorithm proceeds by optimizing a fitness function using Differential Evolution to form the initial clusters. The clusters obtained after the initial phase are then “refined” by re-evaluating the points that fall at the fringes of the clusters and reassigning them to other clusters, if necessary. Two different approaches e.g. Nearest Cluster Based Re-evaluation (N-CBR) and Multiple Cluster Based Re-evaluation (M-CBR) have been proposed to select candidates during the reassignment phase and their performances have been evaluated. The result of such a post processing phase has been demonstrated on a number of standard benchmark text corpora and the algorithm is found to be quite accurate and efficient. The results obtained by the proposed method have also been compared to other evolutionary strategies e.g. Genetic Algorithm(GA), Particle Swarm Optimization(PSO), Harmony Search(HS), and have been found to be quite satisfactory.

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Notes

  1. http://sites.labic.icmc..usp.br/text_collections

  2. http://mlg.ucd.ie/datasets/bbc.html

  3. https://conda.io/miniconda.html

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  1. Department of CSE, Birla Institute of Technology, Mesra, India

    D. Mustafi & A. Mustafi

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  1. D. Mustafi
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Mustafi, D., Mustafi, A. A differential evolution based algorithm to cluster text corpora using lazy re-evaluation of fringe points. Multimed Tools Appl 82, 32177–32201 (2023). https://doi.org/10.1007/s11042-023-14716-3

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