Rule induction in cascade model based on sum of squares decomposition

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

Volumn 1704, Issue , 1999, Pages 468-475

  Okada, Takashi ^a  

^a KWANSEI GAKUIN UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING;

CASCADE MODEL; CATEGORICAL VARIABLES; EXPLANATORY POWER; POWER CONCEPTS; RULE INDUCTION; RULE SET; SUM OF SQUARES; SUM OF SQUARES DECOMPOSITIONS;

STREAM FLOW;

EID: 84956861964     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-48247-5_60     Document Type: Conference Paper

References (17)

1. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont CA (1984)
2. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. Proc. ACM SIGMOD (1993) 207-216
3. Okada, T.: Finding Discrimination Rules Based on Cascade Model. submitted to J. Jpn. Soc. Artificial Intelligence (1999)
4. Agarawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. Proc. VLDB (1994) 487-499
5. Takeuchi, K. et al. (eds.): Encyclopedia of Statistics (in Japanese). Toyo Keizai Shinpou (1990) 388-390
6. Light, R.J., Margolin, B.H.: An Analysis of Variance for Categorical Data. J. Amer. Stat. Assoc. 66 (1971) 534-544
7. Okada, T.: Sum of Squares Decomposition for Categorical Data. to appear in Kwansei Gakuin Studies in Computer Science 14 (1999). http://www.media.kwansei.ac.jp/home/kiyou/kiyou99/kiyou99-e.html
8. Okada, T.: Efficient Detection of Local Interactions in Cascade Model. submitted to Discovery Science 99
9. Ziarko, W.: The Discovery, Analysis, and Representation of Data Dependencies in Databases. In: Piatetsky-Shapiro, G., Frawley, W. (eds.): Knowledge Discovery in Databases. AAAI Press (1991) 195-209
10. Hawkins, D.M., Kass, G.V.: Automatic Interaction Detection. In: Hawkins D.M. (ed.): Topics in Applied Multivariate Analysis. Cambridge Univ. Press (1982) 269-302
11. Ali, K., Manganaris, S., Srikant, R.: Partial Classification using Association Rules. Proc. KDD-97 (1997) 115-118
12. Bayardo, R.J.: Brute-Force Mining of High-Confidence Classification Rules. Proc. KDD- 97 (1997) 123-126
13. Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. Proc. KDD-98 (1998) 80-86
14. Silberschatz, A. and Tuzhilin, A.: What Makes Patterns Interesting in Knowledge Discovery Systems. IEEE Trans. on Know. and Data Eng. 8(6) (1996) 970-974
15. Washio, T., Matsuura, H., Motoda, H.: Mining Association Rules for Estimation and Prediction. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) Research and Development in Knowledge Discovery and Data Mining. Lecture Notes in A.I. Vol. 1394. Springer-Verlag (1998) 417-419
16. Suzuki, E., Shimura, M.: Exceptional Knowledge Discovery in Databases based on Information Theory. Proc. KDD-96 (1996) 275-278
17. Silverstein, C., Brin, S., Motwani, R.: Beyond Market Baskets: Generalizing Association Rules to Dependence Rules. Data Mining and Knowledge Discovery, 2 (1998) 39-68