Fast Extraction Method of Functional Clusters from Large-Scale Spatial Networks Based on Transfer Learning

Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas

“…We experimentally evaluated the efficiency of our proposed method, the GS method, in terms of its computation time by comparing the following three baseline methods including our previous methods: the first method, which only employs the Lazy Evaluation (LE) technique (Leskovec et al 2007), is referred to as the (a) LE method; the second method, which employs LE, medoid pruning, and outlier pivot pruning techniques (Fushimi et al 2016b), is called the (b) Pivot Pruning (PP) method, where we set the number of outlier pivots to 10; and the third method, based on the Transfer Learning (TL) technique mentioned in Section 4, is called the (c) TL method (Fushimi et al 2017c). In our experiments, we changed the number of medoids, K, from 2 to 10, the number of dimensionalities of the functional vectors, S, to 10, 100, 1000, and 10,000, and set the number of source networks, M = 14, in the GS method.…”

“…To overcome this difficulty, we proposed an accelerated version of a greedy algorithm for K-medoids clustering, which produces identical results to the original FCE method, by equipping it with some pruning techniques (Fushimi et al 2016b). For further acceleration, by focusing on the structural similarity of urban streets and regarding them as spatial networks, we proposed a transfer learning-based method (Fushimi et al 2017c), which approximates medoid vectors using an already clustered network. We call this a source domain network (source network).…”

“…Therefore, in this paper, based on these observations, we extend the above transfer learning-based method (Fushimi et al 2017c) by using the K medoids of more than one source network and use K medoids of M source networks, where the total number of medoids is MK. For a target network that consists of N nodes, each of whose function is represented as a high-dimensional vector, we select MK candidates of approximate medoids from N nodes based on the cosine similarities among the vectors of the MK medoids and the N nodes.…”