Araştırma Makalesi

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

Cilt: 9 Sayı: 2 30 Haziran 2023
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MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views

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

Kaynakça

  1. [1] Han, J., Pei, J., & Tong, H. (2022). Data mining: concepts and techniques. Morgan kaufmann.
  2. [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. [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. [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. [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. [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. [7] Zhao, J., Xie, X., Xu, X., & Sun, S. (2017). Multi-view learning overview: Recent progress and new challenges. Information Fusion, 38, 43-54.
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Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

30 Haziran 2023

Gönderilme Tarihi

5 Mayıs 2023

Kabul Tarihi

12 Haziran 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 9 Sayı: 2

Kaynak Göster

APA
Al Shehabı, S., & Yıldırım Imamoglu, M. (2023). MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views. International Journal of Computational and Experimental Science and Engineering, 9(2), 141-149. https://doi.org/10.22399/ijcesen.1292987
AMA
1.Al Shehabı S, Yıldırım Imamoglu M. MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views. IJCESEN. 2023;9(2):141-149. doi:10.22399/ijcesen.1292987
Chicago
Al Shehabı, Shadi, ve Meltem Yıldırım Imamoglu. 2023. “MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views”. International Journal of Computational and Experimental Science and Engineering 9 (2): 141-49. https://doi.org/10.22399/ijcesen.1292987.
EndNote
Al Shehabı S, Yıldırım Imamoglu M (01 Haziran 2023) MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views. International Journal of Computational and Experimental Science and Engineering 9 2 141–149.
IEEE
[1]S. Al Shehabı ve M. Yıldırım Imamoglu, “MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views”, IJCESEN, c. 9, sy 2, ss. 141–149, Haz. 2023, doi: 10.22399/ijcesen.1292987.
ISNAD
Al Shehabı, Shadi - Yıldırım Imamoglu, Meltem. “MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views”. International Journal of Computational and Experimental Science and Engineering 9/2 (01 Haziran 2023): 141-149. https://doi.org/10.22399/ijcesen.1292987.
JAMA
1.Al Shehabı S, Yıldırım Imamoglu M. MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views. IJCESEN. 2023;9:141–149.
MLA
Al Shehabı, Shadi, ve Meltem Yıldırım Imamoglu. “MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views”. International Journal of Computational and Experimental Science and Engineering, c. 9, sy 2, Haziran 2023, ss. 141-9, doi:10.22399/ijcesen.1292987.
Vancouver
1.Shadi Al Shehabı, Meltem Yıldırım Imamoglu. MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views. IJCESEN. 01 Haziran 2023;9(2):141-9. doi:10.22399/ijcesen.1292987