Succinct summarization of transactional databases: An overlapped hyperrectangle scheme

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2008, Pages 758-766

  Xiang, Yang ^a   Jin, Ruoming ^a   Fuhry, David ^a   Dragan, Feodor F ^a  

^a KENT STATE UNIVERSITY   (United States)

Author keywords

Hyperrectangle; Set cover; Summarization; Transactional databases

Indexed keywords

APPROXIMATION RATIOS; CO-CLUSTERING; EFFICIENT ALGORITHMS; FALSE POSITIVES; FREQUENT ITEM SETS; HIGH-LEVEL STRUCTURES; HYPER-RECTANGLES; HYPERRECTANGLE; NP-HARD; POLYNOMIAL-TIME; PRUNING STRATEGIES; RESEARCH PROBLEMS; SET COVER; SET COVERING PROBLEMS; SPEED-UP; SUMMARIZATION; SYNTHETIC DATA SETS; TRANSACTIONAL DATABASES; TRANSACTIONAL DATUM;

APPROXIMATION ALGORITHMS; DATA MINING; ENCODING (SYMBOLS); INFORMATION MANAGEMENT; MINING; POLYNOMIAL APPROXIMATION;

PROBLEM SOLVING;

EID: 65449190237     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1401890.1401981     Document Type: Conference Paper

References (19)

1. Foto N. Afrati, Aristides Gionis, and Heikki Mannila. Approximating a collection of frequent sets. In KDD, pages 12-19, 2004.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In 20th VLDB Conf, September 1994.
3. Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, and Prabhakar Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD Conference, pages 94-105, 1998.
4. Christan Borgelt. Apriori implementation. http://fuzzy.cs.Uni-Magdeburg. de/borgelt/Software. Version 4.08.
5. Varun Chandola and Vipin Kumar. Summarization - compressing data into an informative representation. Knowl. Inf. Syst., 12(3):355-378, 2007.
6. V. Chvátal. A greedy heuristic for the set-covering problem. Math. Oper. Res, 4:233-235, 1979.
7. Christos Faloutsos and Vasileios Megalooikonomou. On data mining, compression, and kolmogorov complexity. Data Min. Knowl. Discov., 15(1):3-20, 2007.
8. Byron J. Gao and Martin Ester. Turning clusters into patterns: Rectangle-based discriminative data description. In ICDM, pages 200-211, 2006.
9. David Johnson, Shankar Krishnan, Jatin Chhugani, Subodh Kumar, and Suresh Venkatasubramanian. Compressing large boolean matrices using reordering techniques. In VLDB'2004, pages 13-23. VLDB Endowment, 2004.
10. D. Pisinger Kellerer, Hans; U. Pferschy. Knapsack Problems. Springer Verlag, 2005.
11. Laks V. S. Lakshmanan, Raymond T. Ng, Christine Xing Wang, Xiaodong Zhou, and Theodore J. Johnson. The generalized mdl approach for summarization. In VLDB '02, pages 766-777, 2002.
12. Tao Li. A general model for clustering binary data. In KDD, pages 188-197, 2005.
13. Sara C Madeira and Arlindo L. Oliveira. Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 1(1):24-45, 2004.
14. Jian Pei, Guozhu Dong, Wei Zou, and Jiawei Han. Mining condensed frequent-pattern bases. Knowl. Inf. Syst., 6(5):570-594, 2004.
15. Arno Siebes, Jilles Vreeken, and Matthijs van Leeuwen. Item sets that compress. In SDM, 2006.
16. Michael Steinbach, Pang-Ning Tan, and Vipin Kumar. Support envelopes: a technique for exploring the structure of association patterns. In KDD '04, pages 296-305, New York, NY, USA, 2004. ACM.
17. Matthijs van Leeuwen, Jilles Vreeken, and Arno Siebes. Compression picks item sets that matter. In PKDD, pages 585-592, 2006.
18. Jilles Vreeken, Matthijs van Leeuwen, and Arno Siebes. Characterising the difference. In KDD '07, pages 765-774, 2007.
19. Jianyong Wang and George Karypis. On efficiently summarizing categorical databases. Knowl. Inf. Syst., 9(1): 19-37, 2006.