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» Clustering Improves the Exploration of Graph Mining Results
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ICDM
2009
IEEE
153views Data Mining» more  ICDM 2009»
13 years 8 months ago
A New Clustering Algorithm Based on Regions of Influence with Self-Detection of the Best Number of Clusters
Clustering methods usually require to know the best number of clusters, or another parameter, e.g. a threshold, which is not ever easy to provide. This paper proposes a new graph-b...
Fabrice Muhlenbach, Stéphane Lallich
CEC
2005
IEEE
14 years 4 months ago
Improvements to the scalability of multiobjective clustering
In previous work, we have proposed a novel approach to data clustering based on the explicit optimization of a partitioning with respect to two complementary clustering objectives ...
Julia Handl, Joshua D. Knowles
BMCBI
2010
164views more  BMCBI 2010»
13 years 8 months ago
Merged consensus clustering to assess and improve class discovery with microarray data
Background: One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a...
T. Ian Simpson, J. Douglas Armstrong, Andrew P. Ja...
BMCBI
2006
141views more  BMCBI 2006»
13 years 11 months ago
Maximum common subgraph: some upper bound and lower bound results
Background: Structure matching plays an important part in understanding the functional role of biological structures. Bioinformatics assists in this effort by reformulating this p...
Xiuzhen Huang, Jing Lai, Steven F. Jennings
AAAI
2008
14 years 1 months ago
Clustering on Complex Graphs
Complex graphs, in which multi-type nodes are linked to each other, frequently arise in many important applications, such as Web mining, information retrieval, bioinformatics, and...
Bo Long, Zhongfei (Mark) Zhang, Philip S. Yu, Tian...