Efficient detection of local interactions in the cascade model

Abstract

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.