Top-Down Induction of Fuzzy Pattern Trees

Top-Down Induction of Clustering Trees

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 o...

Top-down induction of logical decision trees

Top-down induction of decison trees (TDIDT) is a very popular machine learning technique. Up till now, it has mainly used for propositional learning, but seldomly for relational learning or inductive logic programming. The main contribution of this paper is the introduction of logic decision trees, which make it possible to use TDIDT in inductive logic programming. An implementation of this top...

Top-Down Induction of Phylogenetic Trees

We propose a novel distance based method for phylogenetic tree reconstruction. Our method is based on a conceptual clustering method that extends the well-known decision tree learning approach. It starts from a single cluster and repeatedly splits it into subclusters until all sequences form a different cluster. We assume that a split can be described by referring to particular polymorphic loca...

Top-Down Induction of First-Order Logical Decision Trees

Top-down Randomization of Weighted Search Trees

Aragon and Seidel 1] presented a solution of the dictionary problem which became very popular in the last years. Their algorithm is easy to implement and fast in practice. The main idea consists in simulating a random input by randomly generated priorities assigned to each key. But this method has two substantial drawbacks. The rst one is the large dependence of the performance and correctness ...