Many real world systems can be modeled as networks or graphs. Clustering algorithms that help us to organize and understand these networks are usually referred to as, graph based c...
Covariate shift is a situation in supervised learning where training and test inputs follow different distributions even though the functional relation remains unchanged. A common...
Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen...
We study the interaction between global and local techniques in data mining. Specifically, we study the collections of frequent sets in clusters produced by a probabilistic clust...
Clustering large data sets of high dimensionality has always been a serious challenge for clustering algorithms. Many recently developed clustering algorithms have attempted to ad...
Forming consensus clusters from multiple input clusterings can improve accuracy and robustness. Current clustering ensemble methods require specifying the number of consensus clust...
Pu Wang, Carlotta Domeniconi, Kathryn Blackmond La...