On efficiently summarizing categorical databases

Knowledge and Information Systems

Volumn 9, Issue 1, 2006, Pages 19-37

  Wang, Jianyong ^a   Karypis, George ^b  

^a TSINGHUA UNIVERSITY   (China)

^b UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Categorical database; Clustering; Data mining; Frequent itemset

Indexed keywords

DATA MINING; DATABASE SYSTEMS;

CATEGORICAL DATA; CATEGORICAL DATABASE; CLUSTERING; DOCUMENT CLUSTERING; FREQUENT ITEMSET; FREQUENT ITEMSET MINING; SEARCH SPACE PRUNING; TRANSACTION DATABASE;

CLUSTERING ALGORITHMS;

EID: 32544438259     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-005-0216-7     Document Type: Article

References (35)

1. Agarwal, R., Aggarwal, C., Prasad, V.: A tree projection algorithm for generation of frequent item sets. J Parallel Distrib Comput 61 (3), 350-371 (2001)
2. Agrawal, R., Imielinski, T., Swami, A.: Mining associations between sets of items in massive databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207-216. Washington DC (1993)
3. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of 20th International Conference on Very Large Data Bases, pp. 487-499. Santiago de Chile, Chile (1994)
4. Antonie, M., Zaiane, O.: Text document categorization by term association. In: Proceedings of the 2002 IEEE International Conference on Data Mining, pp. 19-26. Maebashi City, Japan (2002)
5. Bayardo, R.J.: Brute-force mining of high-confidence classification rules. In: Heckerman, D., Mannila, H., Pregibon, D. (eds.) Proceedings of the 3rd International Conference on Knowledge Discovery and Data mining), pp. 123-126. Newport Beach, California, USA (1997)
6. Bayardo, R.J.: Efficiently mining long patterns from databases. In: Haas, L.M., Tiwary, A. (eds.) Proceedings ACM SIGMOD International Conference on Management of Data, pp. 85-93. Seattle, Washington (1998)
7. Beil, F., Ester, M., Xu, X.: Frequent term-based text clustering. In: KDD'02 Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 436-442. Edmonton, Alberta, Canada (2002)
8. BouliAaut, J., Bykowski, A., Rigotti, C.: Free-sets: a condensed representation of Boolean data for the approximation of frequency queries. J Data Mining Knowl Discovery 7 (1), 5-22 (2003)
9. Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Peckham, J. (ed.) Proceedings ACM SIGMOD International Conference on Management of Data, pp. 255-264. Tucson, Arizona, USA (1997)
10. BArdick, D., Calimlim, M., Gehrke, J.: MAFIA: a maximal frequent itemset algorithm for transactional databases. In: Proceedings of the 17th International Conference on Data Engineering, pp. 443-452. Heidelberg, Germany (2001)
11. Fung, B., Wang, K., Ester, M.: Hierachical document clustering using frequent itemsets. In: Barbara, D., Kamath, C. (eds.) Proceedings of the 3rd SIAM International Conference on Data Mining. USA, San Francisco, CA (2003)
12. Gade, K., Wang, J., Karypis, G.: Efficient closed pattern mining in the presence of tough block constraints. In: Kim, W., Kohavi, R., Gehrke, J., DuMouchel, W. (eds.) Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, pp. 138-147. Washington, USA (2004)
13. GaCti, V., Gehrke, J., Ramakrishnan, R.: CACTUS: clustering categorical data using Summaries. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 73-83. San Diego, CA, USA (1999)
14. Auha, S., Rastogi, R., Shim, K.: ROCK: a robut clustering algorithm for categorical attributes. In: Proceedings of the 15th International Conference on Data Engineering, pp. 512-521. Sydney, Austrialia (1999)
15. Gunopulos, D., Mannila, H., Saluja, S.: Discovering all most specific sentences by randomized algorithms. In: Afrati, F.N., Kolaitis, P.G. (eds.) Proceedings of the 6th International Conference on Database Theory, pp. 215-229. Delphi, Greece (1997)
16. Goethals, B., Zaki, M.: Advances in frequent itemset mining implementations: Introduction to FIMI03. In: Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations. Melbourne, Florida, USA (2003)
17. Grahne, G., Zhu, J.: Efficiently using prefix-trees in mining frequent itemsets. In: Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations. Melbourne, Florida, USA (2003)
18. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Chen, W., Naughton, J.F., Bernstein, P.A. (eds.) Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 1-12. Dallas, Texas, USA (2000)
19. Ai, W., Han, J., Pei, J.: CMAR: accurate and efficient classification based on multiple class-association rules. In: Cercone, N., Lin, T.Y., Wu, X. (eds.) Proceedings of the 2001 IEEE International Conference on Data Mining, pp. 369-376. San Jose, California, USA (2001)
20. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Agrawal, R., Stolorz, P.E., Piatetsky-Shapiro, G. (eds.) Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 80-86. New York City, New York, USA (1998)
21. Liu, G., Lu, H., Lou, W., Yu, J.X.: On computing, storing and querying frequent patterns. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 607-612. Washington, District of Columbia, USA (2003)
22. Mannila, H., Toivonen, H.: Multiple uses of frequent sets and condensed representations. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 189-194. Portland, Oregon, USA (1996)
23. Pan, F., Cong, G., Tung, A.K.H., Yang, J., Zaki, M.: CARPENTER: finding closed patterns in long biological datasets. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 637-642. Washington, District of Columbia, USA (2003)
24. Park, J., Chen, M., Yu, P.S.: An effective hash based algorithm for mining association rules. In: Carey, M.J., Schneider, D.A. (eds.) Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, pp. 175-186. San Jose, California (1995)
25. Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Beeri, C., Buneman, P. (eds.) Proceedings of the 6th International Conference on Database Theory, pp. 398-416. Jerusalem, Israel (1999)
26. Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-mine: hyper-structure mining of frequent patterns in large databases. In: Cercone, N., Lin, T.Y., Wu, X. (eds.) Proceedings of the 2001 IEEE International Conference on Data Mining, pp. 441-448. San Jose, California, USA (2001)
27. PeA, J., Han, J., Mao, R.: CLOSET: an efficient algorithm for mining frequent closed itemsets. In: Gunopulos, D., Rastogi, R. (eds.) Proceedings of 2000 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 21-30. Dallas, Texas, USA (2000)
28. SenA, M., Karypis, G.: LPMiner: an algorithm for finding frequent itemsets using length-decreasing support constraint. In: Cercone, N., Lin, T.Y., Wu, X. (eds.) Proceedings of the 2001 IEEE International Conference on Data Mining, pp 505-512. San Jose, California, USA (2001)
29. Toivonen, H.: Sampling large databases for association rules. In: Vijayaraman, T.M., Buchmann, A.P., Mohan, C., Sarda, N.L. (eds.) Proceedings of 22th International Conference on Very Large Data Bases, pp 134-145. Mumbai, India (1996)
30. Wang, J., Han, J., Pei, J.: CLOSET+: Searching for the best strategies for mining frequent closed itemsets. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 236-245. Washington, District of Columbia, USA (2003)
31. Wang, J., Karypis, G.: BAMBOO: Accelerating closed itemset mining by deeply pushing the length-decreasing support constraint. In: Proceedings of the 4th SIAM International Conference on Data Mining. Lake Buena Vista, Florida, USA (2004)
32. Wang, K., Xu, C., Liu, B.: Clustering transactions using large items. In: Proceedings of the 1999 ACM CIKM International Conference on Information and Knowledge Management, pp 483-490. Kansas City, Missouri, USA (1999)
33. XIong, H., Steinbach, M., Tan, P., Kumar, V.: HICAP: Hierarchial clustering with pattern preservation. In: Proceedings of the 4th SIAM International Conference on Data Mining. Lake Buena Vista, Florida, USA (2004)
34. Yiu, M., Mamoulis, N.: Frequent pattern based iterative projected clustering. In: Proceedings of the 3rd IEEE International Conference on Data Mining, pp. 689-692. Melbourne, Florida, USA (2003)
35. Zaki, M., Hsiao, C.: CHARM: an Efficient algorithm for closed itemset mining. In: Grossman, R.L., Han, J., Kumar, V., Mannila, H., Motwani, R. (eds.) Proceedings of the 4th SIAM International Conference on Data Mining. Arlington, VA, USA (2002)