Presenting a method to reduce the sensitivity of incremental clustering algorithms of XML documents based on collective intelligence algorithms

Document Type : Original Article


Department of Management, Faculty of Management, University of Science and Research, Tehran, Iran


Until now, various methods have been presented for storing and retrieving information of semi-structured documents, most of them are placed in two groups with batch and incremental approach. In the batch or cluster approach, it is assumed that all the documents can be accessed and clustered, and the documents can be processed several times, which increases the execution time of such algorithms. In the incremental approach, all the documents do not exist in one place, but over time, they are provided to the classification method, and from this point of view, the execution time of such algorithms is less compared to the batch method, and as a result, their execution speed is faster. In this research, our proposed method was compared with XCLS and XCLS+ methods in three evaluation criteria: Entropy, Purity and Fscore. The results showed that the proposed method is preferable to the XCLS and XCLS+ methods in terms of Entropy, Purity and Fscore, and it is slightly less efficient than the XCLS+ method only in the Fscore criterion.


Main Subjects

Algergawy, A., Mesiti, M., Nayak, R., & Saake, G. (2011). XML data clustering: An overview. ACM Computing Surveys (CSUR), 43(4), 1-41.
Alishahi, M., Naghibzadeh, M., & Aski, B. S. (2010). Tag name structure-based clustering of XML documents. International Journal of Computer and Electrical Engineering, 2(1), 119.
Costa, Gianni, Giuseppe Manco, Riccardo Ortale, and Ettore Ritacco. "Hierarchical clustering of XML documents focused on structural components." Data & Knowledge Engineering 84 (2013): 26-46.
Di Caprio, D., Ebrahimnejad, A., Alrezaamiri, H., & Santos-Arteaga, F. J. (2022). A novel ant colony algorithm for solving shortest path problems with -fuzzy arc weights. Alexandria Engineering Journal, 61(5), 3403-3415.
Eberhart, R. C., & Shi, Y. (2001). Particle swarm optimization: developments, applications and resources. In evolutionary computation, 2001. Proceedings of the 2001 Congress on (Vol. 1, pp. 81-86). IEEE.
Fister, I., Yang, X. S., & Brest, J. (2013). A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13, 34-46.
Gad, A. G. (2022). Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Archives of Computational Methods in Engineering, 1-31.
Gürel, G. (2008). Mining XML documents with association rule algorithms (Doctoral dissertation, Izmir Institute of Technology (Turkey)).
Hwang, J. H., & Ryu, K. H. (2010). A weighted common structure based clustering technique for XML documents. Journal of Systems and Software, 83(7), 1267-1274.
James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627.
Kim, J., & Kim, H. J. (2004). A partition index for XML and semi-structured data. Data & Knowledge Engineering, 51(3), 349-368.
Mishra, S., Shaw, K., & Mishra, D. (2012). A new meta-heuristic bat inspired classification approach for microarray data. Procedia Technology, 4, 802-806.
Nayak, R. (2008). Fast and effective clustering of XML data using structural information. Knowledge and Information Systems, 14(2), 197-215.
Nayak, R. (2008). XML data mining: Process and applications. Idea Group Inc./IGI Global, 22.
Nayak, R., & Tran, T. (2007). A progressive clustering algorithm to group the XML data by structural and semantic similarity. International Journal of Pattern Recognition and Artificial Intelligence, 21(04), 723-743.
Piernik, M., Brzezinski, D., & Morzy, T. (2016). Clustering XML documents by patterns. Knowledge and Information Systems, 46(1), 185-212.
Santos, L., Coutinho-Rodrigues, J., & Current, J. R. (2010). An improved ant colony optimization based algorithm for the capacitated arc routing problem. Transportation Research Part B: Methodological, 44(2), 246-266.
Yesodha, R., & Amudha, T. (2022). A bio-inspired approach: Firefly algorithm for Multi-Depot Vehicle Routing Problem with Time Windows. Computer Communications, 190, 48-56.
Zan, Z., Cong, Y., & Zhang, X. (2022, May). An Improved Bat Algorithm for Solving Nonlinear Algebraic Systems of Equations. In Proceedings of the 7th International Conference on Big Data and Computing (pp. 75-81).
Zang, H., Zhang, S., & Hapeshi, K. (2010). A review of nature-inspired algorithms. Journal of Bionic Engineering, 7, S232-S237.