By Marianna Bolla
ISBN-10: 1118344928
ISBN-13: 9781118344927
Explores normal constructions in graphs and contingency tables by means of spectral conception and statistical methods
This e-book bridges the distance among graph idea and facts through giving solutions to the tough questions which come up whilst statisticians are faced with huge weighted graphs or oblong arrays. Classical and glossy statistical tools acceptable to organic, social, conversation networks, or microarrays are offered including the theoretical historical past and proofs.
This e-book is acceptable for a one-semester direction for graduate scholars in information mining, multivariate facts, or utilized graph conception; yet by way of skipping the proofs, the algorithms is additionally utilized by experts who simply are looking to retrieve details from their info while analysing communique, social, or organic networks.
Spectral Clustering and Biclustering:
- Provides a unified remedy for edge-weighted graphs and contingency tables through tools of multivariate statistical research (factoring, clustering, and biclustering).
- Uses spectral embedding and leisure to estimate multiway cuts of edge-weighted graphs and bicuts of contingency tables.
- Goes past the expanders through describing the constitution of dense graphs with a small spectral hole through the structural eigenvalues and eigen-subspaces of the normalized modularity matrix.
- Treats graphs like statistical info via combining tools of graph conception and statistics.
- Establishes a standard define constitution for the contents of every set of rules, acceptable to networks and microarrays, with unified notions and principles.
Read Online or Download Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables PDF
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Additional info for Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables
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Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables by Marianna Bolla
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