Top-down induction of clustering trees

TL;DR

This paper introduces TIC, a novel top-down clustering method based on decision tree induction and instance-based learning, applicable to propositional and relational domains, with experimental validation.

Contribution

It adapts decision tree induction for clustering using inductive logic programming principles, creating a new system for first-order clustering.

Findings

Effective clustering in propositional and relational domains

Demonstrated the approach's viability through experiments

Integrates decision trees with inductive logic programming

Abstract

An approach to clustering is presented that adapts the basic top-down induction of decision trees method towards clustering. To this aim, it employs the principles of instance based learning. The resulting methodology is implemented in the TIC (Top down Induction of Clustering trees) system for first order clustering. The TIC system employs the first order logical decision tree representation of the inductive logic programming system Tilde. Various experiments with TIC are presented, in both propositional and relational domains.