文章基本信息

标题：An Efficient High Dimensional Cluster Method and its Application in Global Climate Sets
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作者：Ke Li ; Fan Lin ; Kunqing Xie 等
期刊名称：Data Science Journal
电子版ISSN：1683-1470
出版年度：2015
卷号：6
DOI：10.2481/dsj.6.S690
语种：English
出版社：Ubiquity Press
摘要：Because of the development of modern-day satellites and other data acquisition systems, global climate research often involves overwhelming volume and complexity of high dimensional datasets. As a data preprocessing and analysis method, the clustering method is playing a more and more important role in these researches. In this paper, we propose a spatial clustering algorithm that, to some extent, cures the problem of dimensionality in high dimensional clustering. The similarity measure of our algorithm is based on the number of top-k nearest neighbors that two grids share. The neighbors of each grid are computed based on the time series associated with each grid, and computing the nearest neighbor of an object is the most time consuming step. According to Tobler's "First Law of Geography," we add a spatial window constraint upon each grid to restrict the number of grids considered and greatly improve the efficiency of our algorithm. We apply this algorithm to a 100-year global climate dataset and partition the global surface into sub areas under various spatial granularities. Experiments indicate that our spatial clustering algorithm works well.
关键词：Clustering methods; High dimensionality; Global climate datasets; Spatial data