Abstract
In this paper, we treat the road network of each city as a network and attempt to accelerate extracting functional clusters which means areas that perform similar functions in road network. As a method of extracting a group of nodes having similar functions from the network, we have proposed Functional Cluster Extraction method. In this method, high dimensional vectors based on random walks are clustered by the greedy solution of the K-medoids method, and K functional clusters are extracted. However, it is difficult to hold a similarity matrix of all node pairs for a large network with a large number of nodes like a road network. On the other hand, it has been discovered that the structure of the road network has a similar structure even if the area is different. In this paper, we propose a fast clustering method by extracting approximate medoids from the target network, using the medoid set of networks already clustered, and execute the Voronoi tessellation based on them. Using the actual road network, we evaluate the proposed method from the viewpoint of the correct answer rate (accuracy) and the calculation speed of the approximate solution.
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Acknowledgement
This work was supported by a JSPS Grant-in-Aid for Scientific Research (No.17H01826).
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Fushimi, T., Saito, K., Ikeda, T., Kazama, K. (2018). Fast Extraction Method of Functional Clusters from Large-Scale Spatial Networks Based on Transfer Learning. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_98
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