  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.
