Central Clustering of Attributed Graphs

Issue Title: Theoretical Advances in Data Clustering Partitioning a data set of attributed graphs into clusters arises in different application areas of structural pattern recognition and computer vision. Despite its importance, graph clustering is currently an underdeveloped research area in machin...

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Bibliographic Details
Published in:Machine learning Vol. 56; no. 1-3; pp. 169 - 207
Main Authors: Jain, Brijnesh J., Wysotzki, Fritz
Format: Journal Article
Language:English
Published: Dordrecht Springer Nature B.V 01.07.2004
Subjects:
Algorithms
Competition
Neural networks
Studies
ISSN:0885-6125, 1573-0565
Online Access:Get full text
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Summary:Issue Title: Theoretical Advances in Data Clustering Partitioning a data set of attributed graphs into clusters arises in different application areas of structural pattern recognition and computer vision. Despite its importance, graph clustering is currently an underdeveloped research area in machine learning due to the lack of theoretical analysis and the high computational cost of measuring structural proximities. To address the first issue, we introduce the concept of metric graph spaces that enables central (or center-based) clustering algorithms to be applied to the domain of attributed graphs. The key idea is to embed attributed graphs into Euclidean space without loss of structural information. In addressing the second issue of computational complexity, we propose a neural network solution of the K-means algorithm for structures (KMS). As a distinguishing feature to improve the computational time, the proposed algorithm classifies the data graphs according to the principle of elimination of competition where the input graph is assigned to the winning model of the competition. In experiments we investigate the behavior and performance of the neural KMS algorithm.[PUBLICATION ABSTRACT]
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ISSN:0885-6125
1573-0565
DOI:10.1023/B:MACH.0000033119.52532.ce