Fuzzy clustering of quantitative and qualitative data
Christian Döring, Christian Borgelt, Rudolf Kruse · IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04. · 2004
In many applications the objects to cluster are described by quantitative as well as qualitative features. A variety of algorithms has been proposed for unsupervised classification if fuzzy partitions and descriptive cluster prototypes are desired. However, most of these methods are designed for data sets with variables measured in the same scale type (only categorical, or only metric). We propose a new fuzzy clustering approach based on a probabilistic distance measure. Thus a major drawback of present methods can be avoided which ties in the vulnerability to favor one type of attributes.