Supervised Clustering: Algorithms and Application

This work centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering assumes that the examples are classified and has the goal of identifying class-uniform clusters that have high probability densities. Three representative–based algorithms for supervised clustering are introduced: two greedy algorithms SRIDHCR and SPAM, and an evolutionary computing algorithm named SCEC. The three algorithms were evaluated using a benchmark consisting of UCI machine learning datasets. Study of the solution landscape for the fitness function used by supervised clustering shows that the landscape seems to have a “Canyonland” shape, thereby, increasing the difficulty of the clustering task for the greedy algorithms. Furthermore, we introduce a technique for class decomposition and demonstrate with experimental results how it could enhance the performance of simple classifiers. We, also, present a dataset editing technique, we call supervised clustering editing (SCE), which replaces examples of a learned cluster by the cluster representative. Our experimental results demonstrate how dataset editing techniques in general and SCE technique in particular enhance the performance of NN classifiers. Other potential applications of supervised clustering such as summary generation, discovery of interesting regions in spatial databases, and distance function learning are discussed as well