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A Graph Theoretic Approach to Heterogeneous Data Clustering
New Research Directions and Some Results
Manjeet Rege
Livre broché | Anglais
58,45 €
+ 116 points
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.
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 152
- Langue:
- Anglais
Caractéristiques
- EAN:
- 9783639116588
- Date de parution :
- 27-02-09
- Format:
- Livre broché
- Format numérique:
- Trade paperback (VS)
- Dimensions :
- 152 mm x 229 mm
- Poids :
- 231 g
