Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Rule Induction in Cascade Model Based on Sum of Squares Decomposition
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Medical Knowledge Discovery on the Meningoencephalitis Diagnosis Studied by the Cascade Model
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Datascape Survey Using the Cascade Model
DS '02 Proceedings of the 5th International Conference on Discovery Science
A correlation-based approach to attribute selection in chemical graph mining
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
Spiral mining using attributes from 3d molecular structures
AM'03 Proceedings of the Second international conference on Active Mining
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Detection of interactions among data items constitutes an essential part of knowledge discovery. The cascade model is a rule induction methodology using levelwise expansion of a lattice. It can detect positive and negative interactions using the sum of squares criterion for categorical data. An attribute-value pair is expressed as an item, and the BSS (between-groups sum of squares) value along a link in the itemset lattice indicates the strength of interaction among item pairs. A link with a strong interaction is represented as a rule. Items on the node constitute the left-hand side (LHS) of a rule, and the right-hand side (RHS) displays veiled items with strong interactions with the added item. This implies that we do not need to generate an itemset containing the RHS items to get a rule. This property enables effective rule induction. That is, rule links can be dynamically detected during the generation of a lattice. Furthermore, the BSS value of the added attribute gives an upper bound to those of other attributes along the link. This property gives us an effective pruning method for the itemset lattice. The method was implemented as the software DISCAS. There, the items to appear in the LHS and RHS are easily controlled by input parameters. Its algorithms are depicted and an application is provided as an illustrative example.