| A Graph Theoretic Approach to Heterogeneous Data Clustering Contributor(s): Rege, Manjeet (Author) |
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ISBN: 3639116585 ISBN-13: 9783639116588 Publisher: VDM Verlag OUR PRICE: $60.53 Product Type: Paperback Published: February 2009 |
| Additional Information |
| BISAC Categories: - Computers |
| Physical Information: 0.35" H x 6" W x 9" (0.51 lbs) 152 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |