Mining Generalized Patterns from Large Databases using Ontologies

TL;DR

This paper explores how Formal Concept Analysis can leverage domain ontologies to extract generalized patterns from large databases, enhancing pattern navigation and understanding.

Contribution

It demonstrates the impact of using taxonomies on objects and attributes within FCA to improve pattern extraction and analysis in data mining.

Findings

Analyzed three generalization cases and their effects on pattern set size

Discussed scenarios involving simultaneous generalizations on objects and attributes

Showed how ontologies can enhance pattern navigation and understanding

Abstract

Formal Concept Analysis (FCA) is a mathematical theory based on the formalization of the notions of concept and concept hierarchies. It has been successfully applied to several Computer Science fields such as data mining,software engineering, and knowledge engineering, and in many domains like medicine, psychology, linguistics and ecology. For instance, it has been exploited for the design, mapping and refinement of ontologies. In this paper, we show how FCA can benefit from a given domain ontology by analyzing the impact of a taxonomy (on objects and/or attributes) on the resulting concept lattice. We willmainly concentrate on the usage of a taxonomy to extract generalized patterns (i.e., knowledge generated from data when elements of a given domain ontology are used) in the form of concepts and rules, and improve navigation through these patterns. To that end, we analyze three generalization cases and show their impact on the size of the generalized pattern set. Different scenarios of simultaneous generalizations on both objects and attributes are also discussed