UNIFIED APPROACH TO DEPENDENT AND DISPARATE CLUSTERING OF NONHOMOGENOUS DATA
D.R. Easterling et al · International Journal of Apllied Mathematics · 2019
There are many data mining settings that involve a combination of attribute-valued descriptors over entities as well as specified relationships between these entities.We present an approach to cluster such nonhomogeneous datasets by using the relationships to impose either dependent clustering or disparate clustering constraints.Unlike prior work that views constraints as Boolean criteria, we present a formulation that allows constraints to be satisfied or violated in a smooth manner.This enables us to achieve dependent clustering and disparate clustering using the same optimization framework by merely maximizing versus minimizing the objective function.We present results on both synthetic data as well as several real-world datasets.