Fuzzy Clustering in Software Reusability

Abstract

This paper is concerned with the organization and retrieval of reusable software components with the aid of unsupervised learning. The methods considered of unsupervised learning include FUZZY ISODATA and Kohonen self-organizing maps. The key issues addressed in the study include information retrieval in the presence of incomplete information, and domain specific enhancements of unsupervised learning, including those of partial supervision. The primary intention is to reveal how the learning mechanism can accommodate individual preferences (profile) of the users viewed as a significant component of organization and retrieval algorithms. Numerical examples use a set of MS-DOS system commands and a collection of reusable C++ classes. © 1997 by John Wiley & Sons, Ltd.