Combining multiple interrelated streams for incremental clustering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5566 LNCS, Issue , 2009, Pages 535-552

  Siddiqui, Zaigham Faraz ^a   Spiliopoulou, Myra ^a  

^a OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APRIORI; DATA MINING APPLICATIONS; DIFFERENT SPEED; FOREIGN KEYS; INCREMENTAL CLUSTERING; INCREMENTAL CLUSTERING ALGORITHM; INCREMENTAL MINING; REAL DATA SETS; SLIDING WINDOW; STRUCTURED DATA; UNKNOWN VALUES;

CLUSTERING ALGORITHMS; MANAGEMENT INFORMATION SYSTEMS; MINING;

CLUSTER ANALYSIS;

EID: 69049112118     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02279-1_38     Document Type: Conference Paper

References (22)

1. Blockeel, H., Raedt, L.D.: Top-down induction of first-order logical decision trees. Artificial Intelligence 101(1-2), 285-297 (1998)
2. Dehaspe, L., Toivonen, H.: Discovery of relational association rules. In: Relational Data Mining, pp. 189-212. Springer, Heidelberg (2001)
3. Dehaspe, L., Toivonen, H.: Discovery of frequent datalog patterns. Data Min. Knowl. Discov. 3(1), 7-36 (1999)
4. Dzeroski, S., Lavrǎc, N.: Inductive learning in deductive databases. IEEE TKDE 5(6), 939-949 (1993)
5. Kramer, S., Widmer, G.: Inducing classification and regression trees in first order logic. In: Relational Data Mining, pp. 140-156. Springer, Heidelberg (2001)
6. Kroegel, M.A.: On Propositionalization for Knowledge Discovery in Relational Databases. PhD thesis, Otto-von-Guericke-University Magdeburg, Germany (2003)
7. Lavrǎc, N., Flach, P.: An extended transformation approach to inductive logic programming. ACM Trans. Comput. Logic 2(4), 458-494 (2001)
8. Bartolini, I., Ciaccia, P., Ntoutsi, I., Patella, M., Theodoridis, Y.: A unified and flexible framework for comparing simple and complex patterns. In: Boulicaut, J.- F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS, vol. 3202, pp. 496-499. Springer, Heidelberg (2004)
9. Maddalena, A., Catania, B.: Towards an interoperable solution for pattern management. In: 3rd Int. Workshop on Database Interoperability INTERDB 2007 (in conjunction with VLDB 2007), Vienna, Austria (September 2007)
10. Guha, S., Meyerson, A., Mishra, N., Motwani, R., O'Callaghan, L.: Clustering data streams: Theory and practice. IEEE TKDE 15(3), 515-528 (2003)
11. Beringer, J., Huellermeier, E.: Online clustering of parallel data streams. Data & Knowledge Engineering 58(2), 180-204 (2006)
12. Das, A., Gehrke, J., Riedewald, M.: Approximate join processing over data streams. In: Proc. of SIGMOD 2003 (2003)
13. Srivastava, U., Widom, J.: Memory-limited execution of windowed stream joins. In: Proc. of VLDB 2004, VLDB Endowment, pp. 324-335 (2004)
14. Xie, J., Yang, J., Chen, Y.: On joining and caching stochastic streams. In: Proc. of SIGMOD 2005, pp. 359-370. ACM, New York (2005)
15. Muggleton, S., Raedt, L.D.: Inductive logic programming: Theory and methods. J. Log. Program. 19/20, 629-679 (1994)
16. Emde,W.,Wettschereck, D.: Relational instance based learning. In: Saitta, L. (ed.) Proc. of ICML 1996, pp. 122-130. Morgan Kaufmann, San Francisco (1996)
17. Kirsten, M.,Wrobel, S., Horv'ath, T.: Distance based approaches to relational learning and clustering. In: Rel. Data Mining, pp. 213-230. Springer, Heidelberg (2001)
18. Knobbe, A.J., de Haas, M., Siebes, A.: Propositionalisation and aggregates. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS, vol. 2168, pp. 277-388. Springer, Heidelberg (2001)
19. Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: Yale: Rapid prototyping for complex data mining tasks. In: Ungar, L., Craven, M., Gunopulos, D., Eliassi-Rad, T. (eds.) Proc. of KDD 2006, pp. 935-940. ACM, New York (2006)
20. Kohavi, R., Brodley, C., Frasca, B., Mason, L., Zheng, Z.: KDD-Cup 2000 organizers' report: Peeling the onion. SIGKDD Explorations 2(2), 86-98 (2000)
21. Tan, P.N., Steinbach, M., Kumar, V.: Intro. to Data Mining. Wiley, Chichester (2004)
22. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)