ARC-UI: A visualization tool for associative classifiers

Proceedings of the International Conference on Information Visualisation

Volumn , Issue , 2008, Pages 296-301

  Chodos, David ^a   Zaïane, Osmar ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Associative classifiers; Classification result analysis; Visualization

Indexed keywords

COMPUTER NETWORKS; DECISION SUPPORT SYSTEMS; INFORMATION ANALYSIS; INFORMATION MANAGEMENT; LEARNING SYSTEMS; VISUALIZATION;

ASSOCIATIVE CLASSIFIERS; CLASSIFICATION RESULT ANALYSIS; INTERNATIONAL CONFERENCES; TRAINING DATA SETS;

CLASSIFICATION (OF INFORMATION);

EID: 51749105411     PISSN: 10939547     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IV.2008.35     Document Type: Conference Paper

References (17)

1. R. Agrawal, T. Imieliński, and A. Swami. Mining association rules between sets of items in large databases. SIGMOD Rec., 22(2):207-216, 1993.
2. M. Ankerst, M. Ester, and H. Kriegel. Towards an effective cooperation of the user and the computer for classification. In KDD 2000, pages 179-188, 2000.
3. M. Antonie and O. Zaïane. Text document categorization by term association. IEEE Data Mining (ICDM), pages 19-26, 2002.
4. A. Asuncion and D.J. Newman. UCI machine learning repository, 2007.
5. O. Couturier, T. Hamrouni, S. Ben Yahia, and E. Mephu Nguifo. A scalable association rule visualization towards displaying large amounts of knowledge. In IV '07: Conf. on Information Visualization, pages 657-663, 2007.
6. Usama Fayyad, Georges G. Grinstein, and Andreas Wierse. Information Visualization in Data Mining and Knowledge Discovery. Morgan Kaufmann, 2002.
7. T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Data mining with optimized two-dimensional association rules. ACM Trans. Database Syst., 26(2), 2001.
8. J. Han and M. Kamber. Data Mining, Concepts and Techniques, 2nd edition. Morgan Kaufmann, 2006.
9. TuBao Ho, TrongDung Nguyen, and DucDung Nguyen. Visualization support for a user-centered kdd process. In SIGKDD '02, pages 519-524, 2002.
10. C.K. Leung and C. Carmichael. Frequent itemset visualization. Technical report, U. of Manitoba, 2007.
11. W. Li, J. Han, and J. Pei. CMAR: Accurate and efficient classifcation based on multiple class-association rules. ICDM, pages 369-376, 2001.
12. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. Proc. of SIGKDD, pages 80-86, 1998.
13. B. Poulin, R. Eisner, D. Szafron, P. Lu, R. Greiner, D.S. Wishart, A. Fyshe, B. Pearcy, C. MacDonnell, and J. Anvik. Visual explanation of evidence in additive classifiers. In Proc. Conf. on Innovative Applications of Artificial Intelligence, pages 1-8, 2006.
14. I. Rahal, D. Ren, A. Perera, H. Najadat, W. Perrizo, R. Rahhal, and W. Valdivia. Incremental interactive mining of constrained association rules from biological annotation data with nominal features. In SAC '05: ACM Symposium on Applied computing, 2005.
15. D. Szafron, P. Lu, R. Greiner, D. Wishart, Z. Lu, B. Poulin, R. Eisner, J. Anvik, and C. MacDonnell. Proteome analyst - transparent high-throughput protein annotation: Function, localization and custom predictors. Technical report, U. of Alberta, 2003.
16. A. Tuzhilin and G. Adomavicius. Handling very large numbers of association rules in the analysis of microarray data. In Proc. ACM SIGKDD, 2002.
17. I.H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufman, 2nd edition edition, 2005.