A Subspace Clustering Framework for Research Group Collaboration

International Journal of Information Technology and Web Engineering (IJITWE)

Volumn 1, Issue 1, 2006, Pages 35-58

  Agarwal, Nitin ^a   Haque, Ehtesham ^a   Liu, Huan ^a   Parsons, Lance ^a  

^a ARIZONA STATE UNIVERSITY   (United States)

Author keywords

collaborative filtering; high dimensional data; highly sparse data; recommender systems; research paper domain; subspace clustering

Indexed keywords

EID: 85001785546     PISSN: 15541045     EISSN: 15541053     Source Type: Journal    
DOI: 10.4018/jitwe.2006010102     Document Type: Article

References (38)

1. Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998). Automatic sub-space clustering of high dimensional data for data mining applications. In Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data (pp. 94–105). ACM Press.
2. Billsus, D., & Pazzani, M. J. (1998). Learning collaborative information filters. In Proceedings of the Fifteenth International Conference on Machine Learning (pp. 46–54). Morgan Kaufmann Publishers Inc.
3. Bollacker, K., Lawrence, S., & Giles, C. L. (1998). CiteSeer: An autonomous Web agent for automatic retrieval and identification of interesting publications. In K. P. Sycara, & M. Wooldridge (Eds.), Proceedings of the Second International Conference on Autonomous Agents (pp. 116–112). New York: ACM Press.
4. Bollacker, K., Lawrence, S., & Giles, C. L. (1999). A system for automatic personalized tracking of scientific literature on the web. In Digital Libraries 99 -The Fourth ACM Conference on Digital Libraries (pp. 105–113). New York: ACM Press.
5. Bradshaw, S., Scheinkman, A., & Hammond, K. J. (2000). Guiding people to information: Providing an interface to a digital library using reference as a basis for indexing. In Intelligent User Interfaces (pp. 37–43).
6. Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence.
7. Chang, J.-W., & Jin, D.-S. (2002). A new cell-based clustering method for large, high-dimensional data in data mining applications. In Proceedings of the 2002 ACM Symposium on Applied Computing (pp. 503–507). ACM Press.
8. Cheng, C.-H., Fu, A. W., & Zhang, Y. (1999). Entropy-based subspace clustering for mining numericaldata. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 84–93). ACM Press.
9. Demiriz, A. (2001). An association mining-based product recommender. In NFORMS Miami 2001 Annual Meeting Cluster: Data Mining.
10. Demiriz, A. (2004). Enhancing product recommender systems on sparse binary data. Data Min. Knowl. Discov., 9(2), 147–170.
11. Deshpande, M., & Karypis, G. (2004). Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst., 22(1), 143–177.
12. Dhillon, I. S., & Guan, Y. (2003). Information theoretic clustering of sparse cooccurrence data. In Proceedings of the Third International Conference on Data Mining. IEEE Press.
13. Giles, C. L., Bollacker, K. D., & Lawrence, S. (1998). CiteSeer: An automatic citation indexing system. In Proceedings of the Third ACM Conference on Digital Libraries (pp. 89–98). ACM Press.
14. Goil, S., Nagesh, H., & Choudhary, A. (1999). Mafia: E±cient and scalable subspace clustering for very large data sets (Technical Report CPDC-TR-9906–010). Northwestern University.
15. Heckerman, D., Chickering, D. M., Meek, C., Rounthwaite, R., & Kadie, C. (2001). Dependency networks for inference, collaborative filtering, and data visualization. Journal of Machine Learning, 1, 49–75.
16. Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5–53.
17. Hofmann, T. (2004). Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 22(1), 89–115.
18. Huang, Z., Chen, H., & Zeng, D. (2004). Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems, 22(1), 116–142.
19. Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). Grouplens: Applying collaborative filtering to usenet news. ACM Communications, 40(3), 77–87.
20. Li, G., Uren, V., Motta, E., Buckingham-Shum, S., & Domingue, J. (2002). ClaiMaker: Weaving a semantic Web of research papers. In Proceedings of the 1st International Semantic Web Conference, Sardinia, June.
21. Lin, C., Alvarez, S., & Ruiz, C. (2000). Collaborative recommendation via adaptive association rule mining. In Proceedings of the International Workshop on Web Mining for E-Commerce (WEBKDD2000).
22. Liu, B., Xia, Y., & Yu, P. S. (2000). Clustering through decision tree construction. In Proceedings of the Ninth International Conference on Information and Knowledge Management (pp. 20–29). ACM Press
23. McNee, S. M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S. K., Rashid, A. M., Konstan, J. A., & Riedl, J. (2002). On the recommending of citations for research papers. In Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work (pp. 116–125). ACM Press.
24. Middleton, S. E., Shadbolt, N. R., & Roure, D. C. D. (2004). Ontological user profiling in recommender systems. ACM Transactions on Information Systems, 22(1), 54–88.
25. Mobasher, B., Cooley, R., & Srivastava, J. (2000). Automatic personalization based on web usage mining. ACM Communications, 43(8), 142–151.
26. Ordonez, C. (2003). Clustering binary data streams with k-means. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (pp. 12–19). ACM Press.
27. Parsons, L., Haque, E., & Liu, H. (2004). Subspace clustering for high dimensional data: A review. ACM SIGKDD Explorations, 6(1), 90–105.
28. Patrikainen, A., & Manilla, H. (2004). Sub-space clustering of high-dimensional binary data — A probabilistic approach. In Workshop on Clustering High Dimensional Data and its Applications, SIAM International Conference on Data Mining.
29. Peters, M., & Zaki, M. J. (2005). Clicks: Clustering categorical data using k-partite maximal cliques. In IEEE International Conference on Data Engineering. IEEE.
30. Procopiuc, C. M., Jones, M., Agarwal, P. K., & Murali, T. M. (2002). A monte carlo algorithm for fast projective clustering. In Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data (pp. 418–427). ACM Press.
31. Resnick, P., & Varian, H. R. (1997). Recommender systems. ACM Communications, 40(3), 56–58.
32. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. In Proceedings of the 2nd ACM Conference on Electronic Commerce (pp. 158–167). ACM Press.
33. Sarwar, B., Karypis, G., Konstan, J., & Reidl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the Tenth International Conference on World Wide Web (pp. 285–295). ACM Press.
34. Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1/2), 115–153.
35. Schein, A., Popescul, A., Ungar, L., & Penncok, D. (2002). Methods and metrics for cold-start recom mendations. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002).
36. Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating word of mouth. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 210–217). ACM Press/Addison-Wesley Publishing Co.
37. Singh, P., Bhimavarapu, R., Davalcu, H., Baral, C., Kim, S., Liu, H., Bittner, M., & Ramakrishnan, I. (2005). Biolog: A browser based collaboration and resource navigation assistant for biomedi-cal researchers. In Proceedings of 2nd International Workshop on Data Integration in the Life Sciences (DILS'05).
38. Ungar, L. H., & Foster, D. P. (1998). Clustering methods for collaborative filtering. In Workshop on Recommendation Systems at the 15th National Conference on Artificial Intelligence.