DIVA: A variance-based clustering approach for multi-type relational data

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2007, Pages 147-156

  Li, Tao ^a   Anand, Sarabjot S ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

Clustering; Multi type; Relational

Indexed keywords

AGGLOMERATIVE CLUSTERING; CLUSTERING; CLUSTERING APPROACHES; CLUSTERING PROCEDURES; CLUSTERING RESULTS; CRITICAL TASKS; DATA OBJECTS; DATA SETS; LARGE DATA SETS; LINEAR TIME COMPLEXITY; LINKAGE INFORMATIONS; MULTI-TYPE; MULTI-TYPE RELATIONAL DATUM; ONLINE RECOMMENDER SYSTEMS; RELATIONAL; RELATIONAL CLUSTERING; RELATIONAL DATUM; REPRESENTATIVE OBJECTS; SEMANTIC INFORMATIONS; VECTOR DATUM;

CLUSTER ANALYSIS; INFORMATION THEORY; KNOWLEDGE MANAGEMENT; ONLINE SYSTEMS;

CLUSTERING ALGORITHMS;

EID: 56549105707     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1321440.1321463     Document Type: Conference Paper

References (16)

1. S. S. Anand, P. Kearney, and M. Shapcott. Generating semantically enriched user profiles for web personalization. ACM Transactions on Internet Technologies, 7(3), August 2007.
2. R. O.Duda, P. E.Hart, andD.G.Stork. Pattern Classification (2nd Edition). Wiley-Interscience Publication, 2001.
3. S. Dzeroski and N. Lavrac. Relational Data Mining. Springer, 2001.
4. M.Ester,H.P. Kriegel, J.Sander, andX. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, 1996.
5. P. Ganesan, H. Garcia-Molina, and J. Widom. Exploiting hierarchical domain structure to compute similarity. ACM Transactions on Information Systems (TOIS), 21(1):64-93, 2003.
6. J. Han and M. Kamber. Data Mining: Concepts and Techniques (2nd Edition). Morgan Kaufmann, 2006.
7. A. K.Jain, M. N. Murty, andP.J.Flynn. Data clustering: a review. ACM Computing Surveys, 31(3):264-323, 1999.
8. M. Kirsten and S. Wrobel. Relational distance-based clustering. In Proceedings of Fachgruppentreffen Maschinelles Lernen (FGML-98), pages 119 - 124, 10587 Berlin, 1998. Techn. Univ. Berlin, Technischer Bericht 98/11.
9. M. Kirsten and S. Wrobel. Extending k-means clustering to first-order representations. In ILP '00: Proceedings of the 10th International Conference on Inductive Logic Programming, pages 112-129, London, UK, 2000. Springer-Verlag.
10. J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R.Gordon, and J. Riedl. GroupLens: Applying collaborative filtering to Usenet news. Communication of the ACM, (3):77-87, 1997.
11. B. Long, Z. M. Zhang, X. Wu;, and P. S. Yu. Spectral clustering for multi-type relational data. In Proceedings of the 23rd international conference on Machine learning (ICML'06), pages 585-592, New York, NY, USA, 2006. ACM Press.
12. J. Neville, M. Adler, and D. Jensen. Clustering relational data using attribute and link information. In Proceedings of the Text Mining and Link Analysis Workshop, 18th International Joint Conference on Artificial Intelligence, 2003.
13. J.Wang,H.Zeng,Z.Chen,H.Lu,T.Li,andW.-Y. Ma. Recom: Reinformcement clustering of multi-type interrelated data objects. In Proceedings of the 26th ACM SIGIR conference on Research and development in informaion retrieval (SIGIR'03), pages 274-281, New York, NY, USA, 2003. ACM Press.
14. R. Xu and D. Wunsch. Survey of clustering algorithms. IEEE Transaction on Neural Networks, 16:645-678, 5 2005.
15. X. Yin, J. Han, and P. S. Yu. Linkclus: efficient clustering via heterogeneous semantic links. In Proceedings of the 32nd international conference on Very large data bases (VLDB'06), pages 427-438. VLDB Endowment, 2006.
16. T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: an efficient data clustering method for very large databases. In Proceedings of 1996 ACM SIGMOD International Conference on Management of Data, pages 103-114, Montreal, Canada, 1996.