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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1044-1045Graph Embedding Method Based on Space Syntax and...

Graph Embedding Method Based on Space Syntax and Improved K-Means Clustering

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Abstract:

The main drawbacks of structural pattern recognition compared to statistical pattern recognition are the high computation complexity and fewer processing tools that are available in the domain. To bridge the gap between the structural and statistical pattern recognition, a new graph embedding method based on space syntax and improved K-means clustering is proposed. The present paper uses the space syntax theory to build quantitative description of the nodes’ topological features, and then combines the proposed topological features with non-topological features in other aspects of the domain to construct node feature set using an improved K-means clustering algorithm, and then maps the graph into vector space explicitly by a statistical approach. Thus SVM can be applied to achieve graph classification. The experimental results show that such an embedding method can achieve higher classification accuracy in different graph datasets.

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Frontiers of Energy, Materials and Information Engineering

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Periodical:

Advanced Materials Research (Volumes 1044-1045)

Pages:

1163-1168

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1044-1045.1163

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Online since:

October 2014

Authors:

Zhi Jie Li, Chang Hua Li, Xin Liu, Pu Liang Zheng

Keywords:

Graph Embedding, K-Means Clustering, Space Syntax, Statistical Pattern Recognition, Structural Pattern Recognition, Topology

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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