AKNOBAS: A knowledge-based segmentation recommender system based on intelligent data mining techniques

Computer Science and Information Systems

Volumn 9, Issue 2, 2012, Pages 713-740

  Rodríguez González, Alejandro ^a   Torres Niño, Javier ^a   Jimenez Domingo, Enrique ^a   Gomez Berbis, Juan Miguel ^a   Alor Hernandez, Giner ^b  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^b INSTITUTO TECNOLÓGICO DE ORIZABA   (Mexico)

Author keywords

Artificial intelligence; Clustering; Data mining; Information systems; Use case

Indexed keywords

EID: 84865243357     PISSN: 18200214     EISSN: None     Source Type: Journal    
DOI: 10.2298/CSIS110722008R     Document Type: Article

References (33)

1. Hartigan, J.A.: Clustering Algorithms. John Wiley & Sons, Inc., 1975
2. Gan, G., Ma, C., Wu, J.: Data Clustering: Theory, Algorithms, and Applications. ASASIAM Series on Statistics and Applied Probability. Vol. 20. (2007)
3. Yuan, S., Cheng, C.: Ontology-based personalized couple clustering for heterogeneous product recommendation in mobile marketing. Expert systems with applications. Vol. 26, No. 4, 461-476. (2004)
4. Han, Y., Shi, P.: An improved ant colony algorithm for fuzzy clustering in image segmentation. Neurocomputing. Vol. 70 No.4-6, 665-671. (2007)
5. Yeo, A.C., Smith, K.A., Willis, R.J., Brooks, M.: Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry. Intelligent Systems in Accounting, Finance & Management. Vol. 10, No. 1, 39-50. (2001)
6. Pronk, N.P., Anderson, L.H., Crain, A.L., Martinson, B.C., O'Connor, P.J., Sherwood, N.E., Whitebird, R.R.: Meeting recommendations for multiple healthy lifestyle factors: Prevalence, clustering, and predictors among adolescent, adult, and senior health plan members. American Journal of Preventive Medicine. Vol. 27, No. 2, 25-33. (2004)
7. Kedes, L.H., Birnstiel, M.L.: Reiteration and Clustering of DNA Sequences Complementary to Histone Messenger RNA. Nature new biology. Vol. 230, 165-169. (1971)
8. Xing, H.J., Hu, B.G.: An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification. Neurocomputing. Vol. 71, No. 4-6, 1008-1021. (2008)
9. G. Huming, and L. Weili,: A Hotel Recommendation System Based on Collaborative Filtering and Rankboost Algorithm. In Proceedings of the Second International Conference on Multi Media and Information Technology, IEEE Computer Society, Washington, DC, USA, 317-320 (2010).
10. F. Juan, L. Wencan.: An Architecture for Collaborative Filtering in Grid Portal Recommendation System. In Proceedings of the Fourth International Conference on Semantics, Knowledge and Grid, IEEE Computer Society, Washington, DC, USA, 359-363. (2008).
11. H. Liang, Y. Xu, Y. Li, R. Nayak.: Collaborative Filtering Recommender Systems Using Tag Information. In Proceedings of the International Conference on Web Intelligence and Intelligent Agent Technology, IEEE Computer Society, Washington, DC, USA, 59-62. (2008).
12. S. Gong, H. Ye, H. Tan.: Combining Memory-Based and Model-Based Collaborative Filtering in Recommender System. In Proceedings of the Conference on Circuits, Communications and System, IEEE Computer Society, Washington, DC, USA, 690-693. (2009).
13. H. Tan, H. Ye.: A Collaborative Filtering Recommendation Algorithm Based On Item Classification. In Proceedings of the Conference on Circuits, Communications and System, IEEE Computer Society, Washington, DC, USA, 694-697. (2009).
14. A. Turati, D. Cerizza, I. Celino, E. Della V.: Analyzing User Actions within a Web 2.0 Portal to Improve a Collaborative Filtering Recommendation System. In Proceedings of the International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, IEEE Computer Society, Washington, DC, USA, 65-68. (2009).
15. Eom, K.B.: Fuzzy clustering approach in unsupervised sea-ice classification. Neurocomputing. Vol. 25, No. 1-3, 149-166. (1999)
16. Ambroise, C., Sèze, G., Badran, F., Thiria, S.: Hierarchical clustering of self-organizing maps for cloud classification. Neurocomputing. Vol. 30, No. 1-4, 47-52. (2000)
17. Lawrence, R.D., Almasi, G.S., Kotlyar, V., Viveros, M.S., Duri, S.S.: Personalization of Supermarket Product Recommendations. Data mining and knowledge discovery. Vol. 5, No. 1-2, 11-32. (2001)
18. Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. ACM Conference On Recommender Systems, ACM, New York, NY, USA, 259-266. (2008)
19. Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Recommender Systems for Large-scale E-Commerce: Scalable Neighborhood Formation Using Clustering. In Proceedings of the Fifth International Conference on Computer and Information Technology. ACM, New York, NY, USA, 19-24. (2002)
20. Wu, Y.H., Chen, Y.C., Chen, A.L.P.: Enabling personalized recommendation on the Web based on user interests and behaviors. Research Issues in Data Engineering, Proceedings of Eleventh International Workshop on Research Issues in Data Engineering. Document Management for Data Intensive Business and Scientific Applications. IEEE Computer Society, Washington, DC, USA, 17-24. (2001)
21. Kim, J.W., Lee, B.H., Shaw, M.J., Chang, H.L., Nelson, M.: Application of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts. International Journal of Electronic Commerce, Vol. 5, No. 5, 45-62. (2001)
22. Corchado, J., Bajom, J. De Paz, Rodríguez S. An execution time neural-CBR guidance assistant. Neurocomputing. Vol. 72, No. 13-15, 2743-2753. (2009)
23. Angles, P., Blattner, D.: System and method for delivering customized advertisements within interactive communication systems. United States patent application publication. (2006)
24. LeMole, S., Howard, S., Thomas, J., Stuntebeck, P.: Method and system for presenting customized advertising to a user on the World Wide Web. United States patent. (1999)
25. Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM. Vol. 40, No. 3, 56-58. (1997)
26. BeRuby. http://www.beruby.com. Last accessed: July, 21. 2011.
27. Han, J., Kamber, M.: Data Mining. Concept and Techniques. Morgan Kaufman. 2nd Edition. (2006)
28. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Willey. 9th Edition. (2005)
29. Pelleg, D., Moore, A.: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 727-734. (2000)
30. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Cluster validity methods: part I. SIGMOD Rec. 31, 2, 40-45. (2002)
31. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Clustering validity checking methods: part II. SIGMOD Rec. 31, 3, 19-27. (2002)
32. Stephan Weibelzahl. Framework for the evaluation of adaptive cbr systems. In Proceedings of the 9th German Workshop on Case-Based Reasoning, 254-263, Baden-Baden, Germany (2001).
33. Evaluation results. Available online at: http://nadir.uc3m.es/alejandro/pubs/beruby/evresults.pdf