Concept Formation by Incremental Conceptual Clustering

88 - 06 Models of Incremental Concept Formation

Incremental Concept Formation Algorithms Based on Galois (Concept) Lattices

The Galois (or concept) lattice produced from a binary relation has been proved useful for many applications. Building the Galois lattice can be considered as a conceptual clustering method since it results in a concept hierarchy. This article presents incremental algorithms for updating the Galois lattice and corresponding graph, resulting in an incremental concept formation method. Different ...

Constructive Induction by Incremental Concept Formation

This paper describes a framework that generates constructive induction schemes for the concept formation system COBWEB. The basis of this framework|context-dependent bias of multi-valued properties|provides a way for allowing COBWEB to deal with continuous and hierarchical property types as a special case of constructive induction. The constructive induction scheme does not introduce learning b...

An Incremental Concept Formation Approach to Learn and Discover from a Clinical Database

The main interest of this research is to discover clinical implications from a large PTCA (Percutaneous Transluminal Coronary Angioplasty) database. A case-based concept formation model D-UNIMEM, modified from Lebowitz's UNIMEM, is proposed for this purpose. In this model, we integrated two kinds of class membership and the index-conjunction class membership. The former is a polythetic clusteri...

Mining Data in Multi-perspectives

Knowledge discover in Data bases (KDD) concerns the development of models which allow the representation and organization of the knowledge hidden in data. Machine learning systems are an alternative to automate the KDD process. In particular, incremental concept formation systems construct hierarchical abstract representations from observations (non-classified description of specific entities, ...