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A Graph Theoretic Approach to Heterogeneous Data Clustering
New Research Directions and Some Results
Manjeet Rege
Paperback | Engels
€ 74,45
+ 148 punten
Omschrijving
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.
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.
Specificaties
Betrokkenen
- Auteur(s):
- Uitgeverij:
Inhoud
- Aantal bladzijden:
- 152
- Taal:
- Engels
Eigenschappen
- Productcode (EAN):
- 9783639116588
- Verschijningsdatum:
- 27/02/2009
- Uitvoering:
- Paperback
- Afmetingen:
- 150 mm x 9 mm
- Gewicht:
- 219 g
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