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Text Clustering Using PSO Based Dynamic Adaptive SOM for Detecting Emergent Trends

Author

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  • Chandrakala D

    (Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore, India)

  • Sumathi S

    (Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, India)

  • Saran Kumar A

    (Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India)

  • Sathish J

    (Senior Software Engineer, Capgemini, India)

Abstract

Detection and realization of new trends from corpus are achieved through Emergent Trend Detection (ETD) methods, which is a principal application of text mining. This article discusses the influence of the Particle Swarm Optimization (PSO) on Dynamic Adaptive Self Organizing Maps (DASOM) in the design of an efficient ETD scheme by optimizing the neural parameters of the network. This hybrid machine learning scheme is designed to accomplish maximum accuracy with minimum computational time. The efficiency and scalability of the proposed scheme is analyzed and compared with standard algorithms such as SOM, DASOM and Linear Regression analysis. The system is trained and tested on DBLP database, University of Trier, Germany. The superiority of hybrid DASOM algorithm over the well-known algorithms in handling high dimensional large-scale data to detect emergent trends from the corpus is established in this article.

Suggested Citation

  • Chandrakala D & Sumathi S & Saran Kumar A & Sathish J, 2019. "Text Clustering Using PSO Based Dynamic Adaptive SOM for Detecting Emergent Trends," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 15(3), pages 64-78, July.
    • Handle: RePEc:igg:jiit00:v:15:y:2019:i:3:p:64-78
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    Cited by:

    1. Gustavo Candela & Rafael C. Carrasco, 2022. "Discovering emerging topics in textual corpora of galleries, libraries, archives, and museums institutions," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(6), pages 820-833, June.

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