Graph Clustering Via a Discrete Uncoupling Process

A discrete uncoupling process for finite spaces is introduced, called the Markov Cluster Process or the MCL process. The process is the engine for the graph clustering algorithm called the MCL algorithm. The MCL process takes a stochastic matrix as input, and then alternates expansion and inflation,...

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Veröffentlicht in:	SIAM journal on matrix analysis and applications Jg. 30; H. 1; S. 121 - 141
1. Verfasser:	Van Dongen, Stijn
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Philadelphia, PA Society for Industrial and Applied Mathematics 01.01.2008
Schlagworte:	Algebra Algorithms Applied mathematics Bioinformatics Clustering Exact sciences and technology Graphs Heuristic Image retrieval Kinases Linear and multilinear algebra, matrix theory Markov processes Mathematics Multivariate analysis Numerical analysis Numerical analysis. Scientific computation Numerical linear algebra Peer to peer computing Probability and statistics Probability theory and stochastic processes Proteins Sciences and techniques of general use Statistics Markov process Invariant Multiplication Random matrix Markov matrix Limit point Similarity Cluster analysis (statistics) Heuristics 05C90 Connected graph Invariance principle Mapping Image Stochastic process Input Column Ordering circulant matrices Behavior Fast algorithm Mathematical expansion Expansion Markov graph Stochastic matrix 05C85 Cluster Columns diagonal similarity Interpretation graph clustering Numerical analysis Symmetric matrix positive semi-definite matrices stochastic uncoupling Linear algebra Positive definite matrix M matrix 68R10 Power Inequality
ISSN:	0895-4798, 1095-7162
Online-Zugang:	Volltext
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