97
Views
3
CrossRef citations to date
0
Altmetric
Special Issue Article

Fuzzy electromagnetic optimisation clustering algorithm for collaborative filtering

ORCID Icon &
Pages 601-616 | Received 06 May 2018, Accepted 21 Jul 2019, Published online: 14 Aug 2019

References

  • Abedinpourshotorban, H., Mariyam Shamsuddin, S., Beheshti, Z., & Jawawi, D. N. A. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8–22.
  • Aggarwal, C. C., & Reddy, C. K. (2014). Data clustering : algorithms and applications. CRC Press.
  • Ahn, H. J. (2008). A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178(1), 37–51.
  • Almazro, D., Shahatah, G., Albdulkarim, L., Kherees, M., Martinez, R., & Nzoukou, W. (2010). A survey paper on recommender systems. Retrieved from http://arxiv.org/abs/1006.5278
  • Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. (2017). A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognition Letters. doi:10.1016/J.PATREC.2017.10.036
  • Ba, Q., Li, X., & Bai, Z. (2013). Clustering collaborative filtering recommendation system based on SVD algorithm. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, (61003281), Beijing, China. 963–967. doi:10.1109/ICSESS.2013.6615466
  • Babu, G. P., & Murty, M. N. (1994). Clustering with evolution strategies. Pattern Recognition, 27(2), 321–329.
  • Bandyopadhyay, S., & Maulik, U. (2002). An evolutionary technique based on K-means algorithm for optimal clustering in RN. Information Sciences, 146(1–4), 221–237.
  • Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203.
  • Birtolo, C., & Ronca, D. (2013). Advances in clustering collaborative filtering by means of fuzzy C-means and trust. Expert Systems with Applications, 40(17), 6997–7009.
  • Birtolo, C., Ronca, D., & Armenise, R. (2011). Improving accuracy of recommendation system by means of item-based fuzzy clustering collaborative filtering. In 2011 11th International Conference on Intelligent Systems Design and Applications (pp. 100–106). IEEE, Cordoba, Spain. doi:10.1109/ISDA.2011.6121638
  • Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, Madison, Wisconsin. 43–52. doi:10.1111/j.1553-2712.2011.01172.x
  • Cai, Y., Leung, H., Li, Q., Min, H., Tang, J., & Li, J. (2014). Typicality-based collaborative filtering recommendation. IEEE Transactions on Knowledge and Data Engineering, 26(3), 766–779.
  • Cheng, L.-C., & Wang, H.-A. (2014). A fuzzy recommender system based on the integration of subjective preferences and objective information. Applied Soft Computing, 18, 290–301.
  • GAN, G. (2019). DATA CLUSTERING IN C++ : an object-oriented approach. CRC PRESS.
  • Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis. Quality and quantity. Wiley Series in Probability and Statistics. (Vol. 14). doi:10.1007/BF00154794
  • Funk, S. (2006). Netflix update: Try this at home. Retrieved from: https://sifter.org/~simon/journal/20061211.html.
  • Gupta, A., & Tripathy, B. K. (2014). A generic hybrid recommender system based on neural networks. 2014 IEEE International Advance Computing Conference (IACC), Gurgaon, India. 1248–1252. doi:10.1109/IAdCC.2014.6779506
  • Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques. Morgan Kaufmann: San Francisco, CA, itd. doi:10.1016/B978-0-12-381479-1.00001-0
  • Hartigan, J. A. (1975). Clustering algorithems. A Wiley Publication in Applied Statistics, 1–351. doi:10.1002/0471725382.scard
  • Huang, S., Ma, J., Cheng, P., Wang, S., Huang, S., Ma, J., & Cheng, P. (2015). 27 A hybrid multigroup coclustering recommendation framework based on information fusion. Article ACM Transactions on Intelligent Systems and Technology, 6(27), 1–22.
  • Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666.
  • Kant, S., & Mahara, T. (2016). Nearest biclusters collaborative filtering framework with fusion. Journal of Computational Science. doi:10.1016/j.jocs.2017.03.018
  • Koohi, H., & Kiani, K. (2016). User based collaborative filtering using fuzzy C-means. Measurement, 91, 134–139.
  • Koren, Y., & Yehuda. (2008). Factorization meets the neighborhood. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08 (p. 426). New York, NY, USA: ACM Press. doi:10.1145/1401890.1401944
  • Koren, Y., & Yehuda. (2010). Factor in the neighbors. ACM Transactions on Knowledge Discovery from Data, 4(1), 1–24.
  • Liu, Q., Chen, E., Xiong, H., Ding, C. H. Q., & Chen, J. (2012). Enhancing collaborative filtering by user interest expansion via personalized ranking. IEEE Transactions on Systems, Man, and Cybernetics, Part B (cybernetics), 42(1), 218–233.
  • Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1(233), 281–297. citeulike-article-id:6083430
  • Mohammadpour, T., Bidgoli, A. M., Enayatifar, R., & Javadi, H. H. S. (2019). Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm. Genomics. doi:10.1016/J.YGENO.2019.01.001
  • Sarwar, B. M., Karypis, G., Konstan, J., & Riedl, J. (2002). Recommender systems for large-scale E-commerce : Scalable neighborhood formation using clustering. Communications, 50(12), 158–167.
  • Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’02, (August), Tampere, Finland. 253. doi:10.1145/564376.564421
  • Shelokar, P. S., Jayaraman, V. K., & Kulkarni, B. D. (2004). An ant colony approach for clustering. Analytica Chimica Acta, 509(2), 187–195.
  • Shen, H., Yang, J., Wang, S., & Liu, X. (2006). Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets. Soft Computing, 10(11), 1061–1073.
  • Tsai, C.-F., & Hung, C. (2012). Cluster ensembles in collaborative filtering recommendation. Applied Soft Computing, 12(4), 1417–1425.
  • Xu, R., & WunschII, D. (2005). Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16(3), 645–678.
  • Xu, R, & WunschII, D. (2005). Survey of clustering algorithms. Ieee Transactions on Neural Networks, 16(3), 645–678. doi: 10.1109/TNN.2005.845141
  • XuXue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., & Chen, Z. (2005). Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’05 (p. 114). New York, New York, USA: ACM Press. doi:10.1145/1076034

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.