Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas
Abstract Dividing a geographical region into some subregions with common characteristics is an important research topic, and has been studied in many research fields such as urban planning and transportation planning. In this paper, by network analysis approach, we attempt to extract functionally si...
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doaj-0fef81425e764e8e879ddc59ccf137942020-11-25T00:22:51ZengSpringerOpenApplied Network Science2364-82282018-07-013111410.1007/s41109-018-0075-2Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areasTakayasu Fushimi0Kazumi Saito1Tetsuo Ikeda2Kazuhiro Kazama3Tokyo University of TechnologyKanagawa UniversityUniversity of ShizuokaWakayama UniversityAbstract Dividing a geographical region into some subregions with common characteristics is an important research topic, and has been studied in many research fields such as urban planning and transportation planning. In this paper, by network analysis approach, we attempt to extract functionally similar regions, each of which consists of functionally similar nodes of a road network. For this purpose, we previously proposed the Functional Cluster Extraction method, which takes a large amount of computation time to output clustering results because it treats too many high-dimensional vectors. To overcome this difficulty, we also previously proposed a transfer learning-based clustering method that selects approximate medoids from the target network using the K medoids of a previously clustered network and divides all the nodes into K clusters. If we select an appropriate network with similar structural characteristics, this method produces highly accurate clustering results. However it is difficult to preliminarily know which network is appropriate. In this paper, we extend this method to ensure accuracy using the K medoids of multiple networks rather than a specific network. Using actual urban streets, we evaluate our proposed method from the viewpoint of the improvement degree of clustering accuracy and computation time.http://link.springer.com/article/10.1007/s41109-018-0075-2Spatial networkFunctional similarityNode clusteringGreedy algorithmTransfer learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Takayasu Fushimi Kazumi Saito Tetsuo Ikeda Kazuhiro Kazama |
spellingShingle |
Takayasu Fushimi Kazumi Saito Tetsuo Ikeda Kazuhiro Kazama Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas Applied Network Science Spatial network Functional similarity Node clustering Greedy algorithm Transfer learning |
author_facet |
Takayasu Fushimi Kazumi Saito Tetsuo Ikeda Kazuhiro Kazama |
author_sort |
Takayasu Fushimi |
title |
Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas |
title_short |
Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas |
title_full |
Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas |
title_fullStr |
Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas |
title_full_unstemmed |
Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas |
title_sort |
improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas |
publisher |
SpringerOpen |
series |
Applied Network Science |
issn |
2364-8228 |
publishDate |
2018-07-01 |
description |
Abstract Dividing a geographical region into some subregions with common characteristics is an important research topic, and has been studied in many research fields such as urban planning and transportation planning. In this paper, by network analysis approach, we attempt to extract functionally similar regions, each of which consists of functionally similar nodes of a road network. For this purpose, we previously proposed the Functional Cluster Extraction method, which takes a large amount of computation time to output clustering results because it treats too many high-dimensional vectors. To overcome this difficulty, we also previously proposed a transfer learning-based clustering method that selects approximate medoids from the target network using the K medoids of a previously clustered network and divides all the nodes into K clusters. If we select an appropriate network with similar structural characteristics, this method produces highly accurate clustering results. However it is difficult to preliminarily know which network is appropriate. In this paper, we extend this method to ensure accuracy using the K medoids of multiple networks rather than a specific network. Using actual urban streets, we evaluate our proposed method from the viewpoint of the improvement degree of clustering accuracy and computation time. |
topic |
Spatial network Functional similarity Node clustering Greedy algorithm Transfer learning |
url |
http://link.springer.com/article/10.1007/s41109-018-0075-2 |
work_keys_str_mv |
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