Summarization - Compressing data into an informative representation

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2005, Pages 98-105

  Chandola, Varun ^a   Kumar, Vipin ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; DATA TRANSFER; OPTIMIZATION; PROBLEM SOLVING; TELECOMMUNICATION TRAFFIC;

ASSOCIATION ANALYSIS; OBJECTIVE FUNCTIONS;

DATA STRUCTURES;

EID: 34548580802     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2005.137     Document Type: Conference Paper

References (21)

1. SKAION Corporation. SKAION Intrusion Detection System Evaluation Data.
2. F. Afrati, A. Gionis, and H. Mannila. Approximating a collection of frequent sets. In KDD '04.
3. R. Agrawal, T. Imieliski, and A. Swami. Mining association rules between sets of items in large databases. In SIGMOD '93.
4. D. Barbara, J. Couto, S. Jajodia, and N. Wu. ADAM: A testbed for exploring the use of data mining in intrusion detection. SIGMOD Rec., 30(4):15-24, 2001.
5. M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. Lof: Identifying density-based local outliers. In SIGMOD '00.
6. T. Calders and B. Goethals. Mining all non-derivable frequent itemsets. In PKDD '02.
7. V. Chandola and V. Kumar. Summarization - compressing data into an informative representation. Technical Report TR 05-024, Dept. of Computer Science, University of Minnesota, Minneapolis, MN, USA, 2005.
8. L. Ertöz, E. Eilertson, A. Lazarevic, P.-N. Tan, V. Kumar, J. Srivastava, and P. Dokas. MINDS - Minnesota Intrusion Detection System. In Data Mining - Next Generation Challenges and Future Directions. MIT Press, 2004.
9. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2000.
10. J. Han, J. Wang, Y. Lu, and P. Tzvetkov. Mining top-k frequent closed patterns without minimum support. In ICDM '02.
11. M. Hu and B. Liu. Mining and summarizing customer reviews. In KDD '04.
12. A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice-Hall, Inc., 1988.
13. G. Karypis. Cluto 2.1.1 software for clustering high-dimensional datasets.
14. R. P. Lippmann et al. Evaluating intrusion detection systems - the 1998 DARPA off-line intrusion detection evaluation. In DISCEX '00, volume 2, pages 12-26, 2000.
15. B. Liu, M. Hu, and W. Hsu. Multi-level organization and summarization of the discovered rules. In KDD '00.
16. M. V. Mahoney and P. K. Chan. Learning non-stationary models of normal network traffic for detecting novel attacks. In KDD '02.
17. I. Mani. Advances in Automatic Text Summarization. MIT Press, Cambridge, MA, USA, 1999.
18. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In ICDT '99.
19. J. Pei, G. Dong, W. Zou, and J. Han. On computing condensed frequent pattern bases. In ICDM '02.
20. S. J. Stolfo, W. Lee, P. K. Chan, W. Fan, and E. Eskin. Data mining-based intrusion detectors: An overview of the Columbia ids project. SIGMOD Rec., 30(4):5-14, 2001.
21. P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining, chapter 8. Addison-Wesley, April 2005.