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International Conference on Computational Science and Its Applications

ICCSA 2017: Computational Science and Its Applications – ICCSA 2017 pp 372–384Cite as

Evaluation of Frequent Pattern Growth Based Fuzzy Particle Swarm Optimization Approach for Web Document Clustering

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10404))

Abstract

Soft and hard clustering efficiency evaluation of novel approach of frequent pattern growth based fuzzy particle swarm optimization for clustering web documents is studied and analyzed in this paper. The conventional approaches K-Means and Fuzzy c-means (FCM) fails with regard to random initialization and local minima hookups. To overcome this drawbacks, bio inspired mechanisms like genetic algorithm, ant colony optimization and particle swarm optimization (PSO) are used to optimize the K-means and FCM clustering. The major contribution of the novel method are three fold. Primarily in its ways to automatically find effective cluster numbers, cluster centroids and swarms for the bio inspired fuzzy particle swarm optimization. Second in yielding fuzzy overlapping clusters using the FCM objective function overcoming the drawbacks of the existing methods. Third, the methodology discusses in this paper prunes out the irrelevant elements from the search space and thereby retains all relationships with search query as semantic conditionally relatable sets. The evaluation results show that our proposed approach performs better for Adjusted Rand Index (ARI), Normalized Mutual Information (NMI) and Adjusted Concordance Index (ACI) against various distance based similarity measures and FCMPSO.

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Author information

Authors and Affiliations

  1. Mahatma Gandhi University, Kottayam, India

    Raja Varma Pamba

  2. Indian Institute of Information Technology Management-Technopark, Thiruvananthapuram, Kerala, India

    Elizabeth Sherly

  3. Payszone LLC LTD, Dubai, UAE

    Kiran Mohan

Authors
  1. Raja Varma Pamba
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  2. Elizabeth Sherly
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  3. Kiran Mohan
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Corresponding author

Correspondence to Raja Varma Pamba .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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© 2017 Springer International Publishing AG

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Pamba, R.V., Sherly, E., Mohan, K. (2017). Evaluation of Frequent Pattern Growth Based Fuzzy Particle Swarm Optimization Approach for Web Document Clustering. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_27

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  • DOI: https://doi.org/10.1007/978-3-319-62392-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62391-7

  • Online ISBN: 978-3-319-62392-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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