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

Document Type : Original Article

Authors

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

Abstract

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.

Keywords

Main Subjects

References

Algergawy, A., Mesiti, M., Nayak, R., & Saake, G. (2011). XML data clustering: An overview. ACM Computing Surveys (CSUR), 43(4), 1-41.  https://doi.org/10.1609/icwsm.v7i1.14380
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.  https://doi.org/10.1609/icwsm.v7i1.21369
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.  https://doi.org/10.1609/icwsm.v7i1.95647
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.  https://doi.org/10.1609/icwsm.v7i1.6257
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.  https://doi.org/10.1609/icwsm.v7i1.62957
Fister, I., Yang, X. S., & Brest, J. (2013). A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13, 34-46.  https://doi.org/10.1609/icwsm.v7i1.62148
Gad, A. G. (2022). Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Archives of Computational Methods in Engineering, 1-31.  https://doi.org/10.1609/icwsm.v7i1.75924
Gürel, G. (2008). Mining XML documents with association rule algorithms (Doctoral dissertation, Izmir Institute of Technology (Turkey)).  https://doi.org/10.1609/icwsm.v7i1.62597
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.  https://doi.org/10.1609/icwsm.v7i1.75391
James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627.  https://doi.org/10.1609/icwsm.v7i1.95173
Kim, J., & Kim, H. J. (2004). A partition index for XML and semi-structured data. Data & Knowledge Engineering, 51(3), 349-368.  https://doi.org/10.1609/icwsm.v7i1.96358
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.  https://doi.org/10.1609/icwsm.v7i1.712596
Nayak, R. (2008). XML data mining: Process and applications. Idea Group Inc./IGI Global, 22.  https://doi.org/10.1609/icwsm.v7i1.7193685
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.  https://doi.org/10.1609/icwsm.v7i1.63215
Piernik, M., Brzezinski, D., & Morzy, T. (2016). Clustering XML documents by patterns. Knowledge and Information Systems, 46(1), 185-212.  https://doi.org/10.1609/icwsm.v7i1.75931
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.  https://doi.org/10.1609/icwsm.v7i1.71937
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.  https://doi.org/10.1609/icwsm.v7i1.93817
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).  https://doi.org/10.1609/icwsm.v7i1.71987
Zang, H., Zhang, S., & Hapeshi, K. (2010). A review of nature-inspired algorithms. Journal of Bionic Engineering, 7, S232-S237.  https://doi.org/10.1609/icwsm.v7i1.63158
 
Send comment about this article
CAPTCHA Image
Engineering Management and Soft Computing
Volume 9, Issue 2 - Serial Number 17
November 2023
Pages 177-187

Files

Share

How to cite

Statistics