Data clustering is the process of automatically grouping data objects into different groups (clusters). The contribution of this book is threefold: homogeneous clustering of images, pairwise heterogeneous data co-clustering, and high-order star-structured heterogeneous data co-clustering. First, we propose a semantic-based hierarchical image clustering framework based on multi-user feedback. By treating each user as an independent weak classifier, we show that combining multi-user feedback is equivalent to the combinations of weak independent classifiers. Second, we present a novel graph theoretic approach to perform pairwise heterogeneous data co-clustering. We then propose Isoperimetric Co-clustering Algorithm, a new method for partitioning the bipartite graph. Lastly, for high-order heterogeneous co-clustering, we propose the Consistent Isoperimetric High-Order Co-clustering framework to address star-structured co-clustering problems in which a central data type is connected to all the other data types. We model this kind of data using a k-partite graph and partition it by considering it as a fusion of multiple bipartite graphs.