Efficient detection of local interactions in the cascade model

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

Volumn 1805, Issue , 2000, Pages 193-203

  Okada, Takashi ^a  

^a KWANSEI GAKUIN UNIVERSITY   (Japan)

Author keywords

Cascade model; Itemset lattice; Local interaction; Pruning of lattice; Sum of squares

Indexed keywords

STREAM FLOW;

CASCADE MODEL; ITEMSET; LOCAL INTERACTIONS; PRUNING OF LATTICE; SUM OF SQUARES;

DATA MINING;

EID: 77649118905     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45571-x_24     Document Type: Conference Paper

References (13)

1. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. Proc ACM SIGMOD (1993) 207-216
2. Ali, K., Manganaris, S., Srikant, R.: Partial Classification using Association Rules. Proc KDD-97 (1997) 115-118
3. Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. Proc KDD-98 (1998) 80-86
4. Meretakis D, WUthrich B.; Classification as Mining and Use of Labeled Itemsets. Proc ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (1999)
5. Silverstein, C, Brin, S, Motwani, R.; Beyond Market Baskets: Generalizing Association Rules to Dependence Rules. Data Mining and Knowledge Discovery, 2 (1998) 39-68
6. Okada, T.: Finding Discrimination Rules using the Cascade Model J Jpn Soc. Artificial IntelUgence, 15 (2000) in press
7. Okada, T.: Rule Induction in Cascade Model based on Sum of Squares Decomposition. Principles of Data Mining and Knowledge Discovery. Proc PKDD'99, 468-475, Lecture Notes in Artificial Intelligence 1704, Springer-Verlag (1999).
8. Okada, T.: Sum of Squares Decomposition for Categorical Data. Kwansei Gakuin Studies in Computer Science 14 (1999) 1-6. http://www.media.kwansei.ac.jp/home/kiyou/kiyou99/kiyou99-e.html
9. Gini, C.W.: Variability and Mutability, contribution to the study of statistical distributions and relations, Studi Economico-Giuridici della R. Universita de Cagliari (1912). Reviewed in Light, R.J., Margolin, B.H.: An Analysis of Variance for Categorical Data J Amer Stat Assoc. 66 (1971) 534-544
10. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. Proc VLDB (1994)487-499
11. Toivonen, H.: Sampling Large Databases for Finding Association Rules. Proc VLDB (1996) 134-145
12. Brin, S., Motwani, R., UUman J. D., Tsur, S.: Dynamic Itemset Counting and Implication Rules for Market Basket Data. Proc ACM SIGMOD (1997) 255-264
13. Mertz, C. J., Murphy, P. M.: UCI repository of machine learning databases, http: //www.ics.uci.edu/~mleam/MLRepository.html, University of California, Irvine, Dept. of Information and Computer Sci. (1996)