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AI
2010
Springer
15 years 3 months ago
Understanding the scalability of Bayesian network inference using clique tree growth curves
Bayesian networks (BNs) are used to represent and ef ciently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to per...
Ole J. Mengshoel
CMOT
2004
141views more  CMOT 2004»
15 years 2 months ago
Small Worlds Among Interlocking Directors: Network Structure and Distance in Bipartite Graphs
We describe a methodology to examine bipartite relational data structures as exemplified in networks of corporate interlocking. These structures can be represented as bipartite gr...
Garry Robins, Malcolm Alexander
BMCBI
2006
122views more  BMCBI 2006»
15 years 3 months ago
Computational reconstruction of transcriptional regulatory modules of the yeast cell cycle
Background: A transcriptional regulatory module (TRM) is a set of genes that is regulated by a common set of transcription factors (TFs). By organizing the genome into TRMs, a liv...
Wei-Sheng Wu, Wen-Hsiung Li, Bor-Sen Chen
ICPR
2002
IEEE
16 years 4 months ago
Fast Hierarchical Clustering Based on Compressed Data
: One way to scale up clustering algorithms is to squash the data by some intelligent compression technique and cluster only the compressed data records. Such compressed data recor...
Erendira Rendon, Ricardo Barandela
RECOMB
2009
Springer
16 years 3 months ago
Finding Biologically Accurate Clusterings in Hierarchical Tree Decompositions Using the Variation of Information
Abstract. Hierarchical clustering is a popular method for grouping together similar elements based on a distance measure between them. In many cases, annotation information for som...
Saket Navlakha, James Robert White, Niranjan Nagar...