Entropy-based robust fuzzy clustering of relational data

Relational data clustering algorithms are proposed to deal with the data represented as the similarity or dissimilarity between each pair of objects. Fuzzy clustering of relational data (FRC) is a recently proposed approach that can handle non-Euclidean distance relational data. Unfortunately, negative values may appear in the clustering process of FRC. Another related algorithm A-P (assignment prototype) applies two different memberships and obtains a more stable minimization procedure. However, the fixed exponent m and sensitivity to initialization make A-P less feasible to some data sets. In this paper, we propose a new entropy-based fuzzy clustering for relational data (EFRC). EFRC and its robust version R-EFRC make use of two types of memberships called partitioning and ranking. Experiments on typical relational data sets and 2-D noisy data sets show that the new algorithm can produce meaningful clustering results and is robust to noise.