On the Use of Fuzzy Constraints in Semisupervised Clustering

This paper introduces Fuzzy HSS, a semisupervised hierarchical clustering approach that uses fuzzy instance-level constraints. These constraints are external information on the shape of fuzzy must-link and fuzzy cannot-link restrictions. They allow uncertainty when indicating whether two instances of a dataset belong to the same group. Fuzzy must-link constraints give a degree of belief of two instances belonging to the same group. Analogously, fuzzy cannot-link constraints indicate the degree of belief of two instances not belonging to the same group. These constraints have been introduced in a hierarchical clustering process, allowing us to obtain the optimal number of groups in a dendrogram when the number of clusters is not known. The optimal amount of constraints needed in the process is determined by means of fuzzy entropy. An extensive experimental study is provided by comparing this fuzzy semisupervised approach with classic unsupervised methods, as well as a crisp semisupervised alternative.