An Effective Multi-level Algorithm for Bisecting Graph

Clustering is an important approach to graph partitioning. In this process a graph model expressed as the pairwise similarities between all data objects is represented as a weighted graph adjacency matrix. The min-cut bipartitioning problem is a fundamental graph partitioning problem and is NP-Compl...

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Bibliographic Details
Published inAdvanced Data Mining and Applications pp. 493 - 500
Main Authors Leng, Ming, Yu, Songnian
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Aspiration Criterion
Boundary Vertex
Computer science; control theory; systems
Data processing. List processing. Character string processing
Exact sciences and technology
Memory organisation. Data processing
Multilevel Algorithm
Neighboring Vertex
Software
Tabu Search
Cluster analysis
Graph decomposition
Data analysis
Tabu search
Similarity
Classification
Adjacency matrix
NP complete problem
Graph theory
Data mining
Weighted graph
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Summary:Clustering is an important approach to graph partitioning. In this process a graph model expressed as the pairwise similarities between all data objects is represented as a weighted graph adjacency matrix. The min-cut bipartitioning problem is a fundamental graph partitioning problem and is NP-Complete. In this paper, we present an effective multi-level algorithm for bisecting graph. The success of our algorithm relies on exploiting both Tabu search theory and the concept of the graph core. Our experimental evaluations on 18 different graphs show that our algorithm produces excellent solutions compared with those produced by MeTiS that is a state-of-the-art partitioner in the literature.
ISBN:3540370250
9783540370253
ISSN:0302-9743
1611-3349
DOI:10.1007/11811305_54