MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views

Yıl 2023, , 141 - 149, 30.06.2023

Shadi Al Shehabı Meltem Yıldırım Imamoglu

https://doi.org/10.22399/ijcesen.1292987

Öz

Data mining involves examining vast quantities of data to uncover valuable insights that can be utilized for making informed decisions and driving business objectives. The study focuses on the task of finding relationships between features belonging to two different views using multi-view model, and proposes a novel approach called MARCMV. This approach extracts multi-view association rules from different views of the same data set using multi-clustering neural model. The study finds that MARCMV outperforms conventional symbolic methods in terms of association rule quality and running time.

Anahtar Kelimeler

Multi-view, Clustering, MultiSOM, MARCMV approach, Multi-view association rule

Kaynakça

• [1] Han, J., Pei, J., & Tong, H. (2022). Data mining: concepts and techniques. Morgan kaufmann.
• [2] Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487-499).
• [3] Han, J., Pei, J., Yin, Y., & Mao, R. (2004). Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data mining and knowledge discovery, 8, 53-87.
• [4] Zaki, M. J., & Hsiao, C. J. (2002, April). CHARM: An efficient algorithm for closed itemset mining. In Proceedings of the 2002 SIAM international conference on data mining (pp. 457-473). Society for Industrial and Applied Mathematics.
• [5] Kanhere, S., Sahni, A., Stynes, P., & Pathak, P. (2021, January). Clustering Based Approach to Enhance Association Rule Mining. In 2021 28th Conference of Open Innovations Association (FRUCT) (pp. 142-150). IEEE.
• [6] Tang, C., Zheng, X., Liu, X., Zhang, W., Zhang, J., Xiong, J., & Wang, L. (2021). Cross-view locality preserved diversity and consensus learning for multi-view unsupervised feature selection. IEEE Transactions on Knowledge and Data Engineering, 34(10), 4705-4716.
• [7] Zhao, J., Xie, X., Xu, X., & Sun, S. (2017). Multi-view learning overview: Recent progress and new challenges. Information Fusion, 38, 43-54.
• [8] Li, Z., Tang, C., Zheng, X., Liu, X., Zhang, W., & Zhu, E. (2022). High-order correlation preserved incomplete multi-view subspace clustering. IEEE Transactions on Image Processing, 31, 2067-2080.
• [9] Lamirel, J. C., & Al Shehabi, S. (2005, September). Efficient Knowledge Extraction using Unsupervised Neural Network Models. In 5th Workshop On Self-Organizing Maps-WSOM 05.
• [10] Lamirel, J. C. (2002, May). MultiSOM: a multimap extension of the SOM model. Application to information discovery in an iconographic context. In Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No. 02CH37290) (Vol. 2, pp. 1790-1795). IEEE.
• [11] M. van Leeuwen and E. Galbrun, “Association discovery in two-view data,” IEEE Trans. Knowl. Data Eng., vol. 27, no. 12, pp. 3190–3202, 2015.
• [12] Polanco, X., François, C., & Lamirel, J. C. (2001). Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach. Scientometrics, 51, 267-292.
• [13] Kohonen, T., Kaski, S., Lagus, K., Salojarvi, J., Honkela, J., Paatero, V., & Saarela, A. (2000). Self organization of a massive document collection. IEEE transactions on neural networks, 11(3), 574-585.
• [14] Lamirel, J. C., Shehabi, S., Francois, C., & Polanco, X. (2004). Using a compound approach based on elaborated neural network for Webometrics: an example issued from the EICSTES Project. Scientometrics, 61(3), 427-441.
• [15] Pasquier, N., Bastide, Y., Taouil, R., & Lakhal, L. (1999). Discovering frequent closed itemsets for association rules. In Database Theory—ICDT’99: 7th International Conference Jerusalem, Israel, January 10–12, 1999 Proceedings 7 (pp. 398-416). Springer Berlin Heidelberg.
• [16] Tan, P. N., & Kumar, V. (2000). Interestingness measures for association patterns: A perspective.
• [17] Dua, D., & Graff, C. (2017). UCI machine learning repository.
• [18] Fournier-Viger, P., Lin, J. C. W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., & Lam, H. T. (2016). The SPMF open-source data mining library version 2. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part III 16 (pp. 36-40). Springer International Publishing.
• [19] Lamirel, J. C., Francois, C., Shehabi, S. A., & Hoffmann, M. (2004). New classification quality estimators for analysis of documentary information: application to patent analysis and web mapping. Scientometrics, 60(3), 445-562.