Contextual fuzzy type-2 hierarchies for decision trees (CoFuH-DT) — An accelerated data mining technique

Abstract

Advanced data mining techniques (ADMT) are very powerful tools for classification, understanding and prediction of object behaviors, providing descriptive relationships between objects such as a customer and a product they intend to buy. ADMT typically consists of classifiers and association techniques, among them, decision trees (DT). However, some important relationships are not readily apparent in a traditional decision tree. In addition, decision trees can grow quite large as the number of dimensions and their corresponding elements increase, requiring significant resources for processing. In either case, rules governing these relationships can be difficult to construct. This paper presents CoFuH-DT, a new algorithm for capturing intrinsic relationships among the nodes of DT, based upon a proposed concept of type-2 fuzzy ldquocontextsrdquo. This algorithm modifies a decision tree, first by generating type-1 fuzzy extensions of the underlying DT criteria or ldquoconditionsrdquo; combining further those extensions into new abstractions overlaid with type-2 contexts. The resulting fuzzy type-2 classification is then able to capture intrinsic relationships that are otherwise non-intuitive. In addition, performing fuzzy setbased operations simplifies the decision tree much faster than traditional search techniques in order to aid in rule construction. Testing presented on a virtual store example demonstrates savings of multiple orders of magnitude in terms of nodes and applicable conditions resulting in 1) reduced complexity of decision tree, 2) ability to data mine difficult to detect interrelationships, 3) substantial acceleration of decision tree search, making it applicable for 4) real-time data mining of new knowledge.