Modeling And Research Of The Spatial Routes Clustering Algorithm

Spatial Data Mining is developed in recent years has broad application prospects of data mining techniques. According to the U.S. National Aeronautics and Space Administration's statistics, more than 80% of the data is related to geographical location. Spatial network activity summarization plays an important role in several application domains such as disaster response, the situation assessment and analysis of criminal behavior and other aspects. In disaster response, activity summarization may be used to provide relief assistance in the aftermath of natural disasters. Therefore, the spatial routes clustering algorithm proposed in this paper, the main contents include:the study of spatial data mining and cluster analysis based on partition method. We propose a novel clustering algorithm, called the K-Main Routes (KMR) algorithm, which finds a network located grouping of activities using routes. The algorithm is based on a new interest measure called incident activity, which counts the activities on each shortest path in the spatial network. We also show the spatial routes clustering algorithm modeling, and analyze the model's accuracy, completeness and complexity. On this basis, it also gives a very detailed case study demonstrating the usefulness of routes discovered by the algorithm, the experimental results and analysis. According to the spatial network characteristics, i.e., continuity of time, we introduce a concept of time granularity,and use real data in MapStudio experimental platform to make the experimental results indicating its computational efficiency.