A Fuzzy Semisupervised Clustering Method: Application to the Classification of Scientific Publications

Abstract

This paper introduces a new method of fuzzy semisupervised hierarchical clustering using fuzzy instance level constraints. It introduces the concepts of fuzzy must-link and fuzzy cannot-link constraints and use them to find the optimum α-cut of a dendrogram. This method is used to approach the problem of classifying scientific publications in web digital libraries. It is tested on real data from that problem against classical methods and crisp semisupervised hierarchical clustering. © Springer International Publishing Switzerland 2014.