Advertisement

Comparative Analysis of K-Means Algorithm and Particle Swarm Optimization for Search Result Clustering

  • Shashi MehrotraEmail author
  • Aditi Sharan
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 165)

Abstract

Clustering is being used to organize search results into clusters with an aim to help a user in accessing relevant information. The paper performs a comparative analysis of the most common traditional clustering algorithms: k-means and nature-inspired algorithm, and Particle Swarm Optimization (PSO). Experiments are conducted over the well-known dataset, AMBIENT, used for topic clustering. Experimental results show the highest recall and F-measure is achieved by the PSO. Though the highest precision is achieved by the k-means algorithm, in most of the topics, PSO shows a better result than the k-means algorithm.

Keywords

Clustering K-means algorithm Particle Swarm Optimization Polysemy 

References

  1. 1.
    Carpineto, C., Romano, G.: Ambient dataset. http://search.fub.it/ambient (2008)
  2. 2.
    Celebi, M.E., Kingravi, H.A., Vela, P.A.: A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst. Appl. 40(1), 200–210 (2013)CrossRefGoogle Scholar
  3. 3.
    Chuang, L.Y., Lin, Y.D., Yang, C.H.: An improved particle swarm optimization for data clustering. In: Proceedings of the International MultiConference of Engineers & Computer Scientist 2012 I, IMECS (2012)Google Scholar
  4. 4.
    Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 38(1), 218–237 (2008)CrossRefGoogle Scholar
  5. 5.
    De Carvalho, F.D.A., Lechevallier, Y., De Melo, F.M.: Partitioning hard clustering algorithms based on multiple dissimilarity matrices. Pattern Recognit. 45(1), 447–464 (2012)Google Scholar
  6. 6.
    Grira, N., Crucianu, M., & Boujemaa, N. (2004). Unsupervised and semi-supervised clustering: a brief survey. A review of machine learning techniques for processing multimedia content, 1, 9–16Google Scholar
  7. 7.
    Huang, C.L., Huang, W.C., Chang, H.Y., Yeh, Y.C., Tsai, C.Y.: Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering. Appl. Soft Comput. 13(9), 3864–3872 (2013)CrossRefGoogle Scholar
  8. 8.
    Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31(8), 651–666 (2010)CrossRefGoogle Scholar
  9. 9.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)CrossRefGoogle Scholar
  10. 10.
    Jensi, R., Jiji, D.G.W.: A survey on optimization approaches to text document clustering. Int. J. Comput. Sci. Appl. (IJCSA) 3(6), 31–44 (2013)CrossRefGoogle Scholar
  11. 11.
    McCallum, A.K.: Bow: a toolkit for statistical language modeling, text retrieval, classification and clustering (1996)Google Scholar
  12. 12.
    Mehrotra, S., Kohli, S., Sharan, A.: To identify the usage of clustering techniques for improving search result of a website. Int. J. Data Min. Model. Manag. 10(3), 229–249 (2018)Google Scholar
  13. 13.
    Mehrotra, S., Kohli, S.: Comparative analysis of k-means with other clustering algorithms to improve search result. In: 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 309–313. IEEE (2015)Google Scholar
  14. 14.
    Mehrotra, S., Kohli, S.: The study of the usage of data analytic and clustering techniques for web elements. In: Proceedings of the ACM Symposium on Women in Research 2016, pp. 118–120. ACM (2016)Google Scholar
  15. 15.
    Mehrotra, S., Kohli, S.: Identifying evolutionary approach for search result clustering. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 3778–3782. IEEE (2016)Google Scholar
  16. 16.
    Mehrotra, S., Kohli, S.: Application of clustering for improving search result of a website. In: Information Systems Design and Intelligent Applications, pp. 349–356. Springer, New Delhi (2016)Google Scholar
  17. 17.
    Mehrotra, S., Kohli, S.: Data clustering and various clustering approaches. In: Intelligent Multidimensional Data Clustering and Analysis, pp. 90–108. IGI Global (2017)Google Scholar
  18. 18.
    Mehrotra, S., Kohli, S., Sharan, A.: An intelligent clustering approach for improving search result of a website. Int. J. Adv. Intell. Parad. (in press).  https://doi.org/10.1504/ijaip.2018.10011466CrossRefGoogle Scholar
  19. 19.
    Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)CrossRefGoogle Scholar
  20. 20.
    Zeng, H.J., He, Q.C., Chen, Z., Ma, W.Y., Ma, J.: Learning to cluster web search results. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 210–217. ACM (2004)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringKoneru Lakshmaiah Education FoundationGunturIndia
  2. 2.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

Personalised recommendations