A Hybrid Approach for Web Search Results Clustering using k-Medoids and Artificial Bee Colony Algorithm
DOI:
https://doi.org/10.59421/61btvb81Keywords:
Web Search Results Clustering, k-medoids Algorithm, IABC Algorithm, Snippets, OptimizationAbstract
Web Search Results Clustering (WSRC) is a useful method for organizing retrieved results into related groups. Existing WSRC approaches fail to cluster web search results effectively due to a number of difficulties such as over dependence on the initial cluster centers, easily falling into local optimum, and lack balancing between the exploration and exploitation capabilities due to the randomness and the limitations of their search strategies. In this paper, a hybrid approach for WSRC using k-medoids algorithm and Improved Artificial Bee Colony (IABC) algorithm is proposed to overcome these problems and to help users to identify effectively relevant information from a huge list of retrieved results. More precisely, the IABC algorithm is employed as the global search optimizer whereas k-medoids algorithm is employed as the local search optimizer. As a result, the proposed approach provide solutions that are locally and global balanced, thus the quality of the generated clusters is improved. The performance of the proposed approach is validated and tested on three well known datasets such as ODP-239, MORESQUE, and 20 Newsgroups. Three common evaluation measures are utilized to evaluate the performance of proposed approach, namely, precision, recall and F-measure. The obtained results of the proposed approach are satisfactory and achieved almost all the best results for all datasets in comparison with other competitive approaches.References
1. P. Verma, N. Kesswani, A. Jain, and P. K. Mishra, Web Mining: Analyzing Traversal Patterns, DeepMisti Publication, 2024.
2. P. S. Sharma, D. Yadav, and R. N. Thakur, "Web Page Ranking using Web Mining Techniques: A Comprehensive Survey", Mobile Information Systems, Hindawi, 2022.
3. M. A. Al-Mojahed and B. M. Al-Maqaleh, " DEADC: Density Extending Algorithm for Data Clustering", 2nd International Conference on Emerging Smart Technologies and Applications(eSmarTA), Ibb, Yemen, pp. 1-8, 2022.
4. M. H. Ahmed, S. Tiun, N. Omar, and N. S. Sani, " Short text clustering algorithms, applications and challengence: A survey", Applied Sciences, MDPI, Vol. 13, Issue 1, 2023.
5. A. J. Obiad, T. Chatterjee, and A. Bhattacharya, " Semantic Web and Web Page Clustering Algorithms: A Landscape View", EAI Endoresd Transactions on Energy Web, 8(33), pp. 1-14, 2020.
6. L. Kaufman, P. Rousseeuw, and Y. Dodge, "Clustering by Means of Medoids", In Statistical Data Analysis Based on the L_1-Norm and Related Methods, North-Holland, Amsterdam, pp. 405-416, 1987.
7. D. Karaboga, " An Idea Based on Honey Bee Swarm for Numerical Optimization", Technical Report (TR06), Computer Engineering Department, Engineering Faculty, Erciyes University, 2005.
8. L. Abualigah, A. H. Gandomi, M. AbdElaziz, A. G. Hussien, A. M. Khasawneh, M. Alshinwan, and E. H. Houssein., "A Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis ", Algorithms, 13(345), pp.1-32, 2020.
9. Y. Celik, " An Enhanced Artifical Bee Colony Algorithm Based on Fitness Weighted Search Strategy", Automatika Journal, 62(3), pp. 300-310, 2021.
10. R. Agarwal, S. Gawade, P. Pirale, and D. Dighe, " Clustering of Web Search Result using Meta-heuristic Algorithm", International Journal of Engineering Research and Technology (IJERT), 7(3), pp. 318-319, 2018.
11. Q. Jin, N. Lin, and Y. Zhang, "K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony", Algorithms, MDPI, 14(53), pp.1-23, 2021.
12. H. Hung, and W. Junzo, " Search Result Clustering through Density Analysis Based K-mediod Method", International Conference on Advanced Applied Informatics(IIAI), pp. 155-160, 2014.
13. Mohit, A. C. Kumari, and M. Sharma, " A Novel Approach to Text Clustering using Shift k-Medoid", International Journal of Scocial Computing and Cyber-Physical Systems, 2(2), pp. 106-118, 2019.
14. C. Oktarina, K. A. Notodiputro, and I. Indahwati, "Comparison of K-Means Clustering Method and K-Medoids on Twitter Data", Indonesian Journal of Statistics and its Applications, vol. 4, pp. 189-202, 2020.
15. C. Ramya, " A Novel PSO Methodolgy for Web Documents Retrival ", International Journal of Computational Linguistics Resreach, 10(3), pp. 67-75, 2019.
16. S. M., H. Hasheminejad, M. Vosoughian, and M. Zamini, " AB2C: Artificial Bee Colony for Culstering", International Journal of Artificial Intelligence, CESER Publications, 18(2), pp. 51-73, 2020.
17. B. Al-Attar, J. Allami, and T. A. Imeer, "A Hybrid Approach for Web Sreach Result Clustering Based on Genetic Algorithm with k-Means", Journal of Theoretical and Applied Information Technology, vol. 99, pp.2722-2733 , 2021.
18. M. G. Alam, and S. Baulkani, "A Hybrid Approach for Web Document Clustering using k-Means and Artificial Bee Colony Algorithm", International Journal of Intelligent Engineering and Systems, vol. 9, pp. 11-20, 2016.
19. A. Kumar, D. Kumar, and S. Jarial, "A Hybrid Clustering Method Based on Improved Artificial Bee Colony and Fuzzy C-Means Algorithm", International Journal of Artificial Intellegence, vol. 15, pp. 24-44, 2017.
20. A. S. Abdulameer, S. Tiun, N. S. Sani, M. Ayob and A. Y. Taha, "Enhanced Clustering Models with wiki-Based K-Nearest Neighbors-Based Representation for Web Search Result Clustering", Journal of King Saud University-Computer and Information Sciences, vol. 34, pp. 840-850, 2022.
21. R. Lee, Natural Languge Processing: ATextbook with Python Implementation, 2nd Edition, Springer, 2025.
22. Han. and M. Kamber, Data Mining Concepts and Techniques, 4th Edition , Morgan Kaufmann Puplishers, 2022.
23. R. C. Balabantaray, C. Sarma, and M. Jha, "Document Clustering using k-Means and k-Medoids", International Journal of Knowledge Based Computer System, 1(1), 2013.
24. .S. Mehrotra, and A. Sharan, " Comparative Analysis of k-Means Algorithm and Particle Swarm Optimization for Search Result Clustering", Smart Trends in Computing and Communications, Springer, vol. 165, pp. 209-114, 2020.
25. C. Carpineto, and G. Romano, " ODP-239 Dataset, 2009, http://credo.fub.it/odb239/.
26. R. Navigli, and G. Crisafullli, " Inducing Word Senses to Improve Web Search Result Clustering", In Proceedings of the 2010 Confrence on Empirical Methods in Natural Language Processing (EMNLP 2010), MIT Stata Center, Massachusetts, USA, pp. 116-126, 2010.
27. L. Blake, and M. J. Merz, UCI Repository of Machine Learning Database [http://www.ics.uci.edu/mlearn/MLRepository.html ], Irvine, CA: University of California, Department of Information and Computer Science.
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Copyright (c) 2026 Ahmed M. Al-Amdi، Basheer M. Al-Maqaleh (مؤلف)
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