所属 コンピュータサイエンス学部 コンピュータサイエンス学科 職種 専任講師
|標題||Improving Approximate Extraction of Functional Similar Regions from Large-Scale Spatial Networks Based on Greedy Selection of Representative Nodes of Different Areas|
|掲載誌名||Applied Network Science|
|著者・共著者||Takayasu Fushimi，Kazumi Saito，Tetsuo Ikeda，Kazuhiro Kazama|
|概要||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.