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An Efficient High Dimensional Cluster Method and its Application in Global Climate Sets

Authors:

Ke Li ,

Department of Intelligence Science, State Key Laboratory of Machine Perception, Peking University, Peking University, Beijing100871, China
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Fan Lin,

Department of Intelligence Science, State Key Laboratory of Machine Perception, Peking University, Peking University, Beijing100871, China
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Kunqing Xie

Department of Intelligence Science, State Key Laboratory of Machine Perception, Peking University, Peking University, Beijing100871, China
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Abstract

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.
DOI: http://doi.org/10.2481/dsj.6.S690
How to Cite: Li, K., Lin, F. & Xie, K., (2007). An Efficient High Dimensional Cluster Method and its Application in Global Climate Sets. Data Science Journal. 6, pp.S690–S697. DOI: http://doi.org/10.2481/dsj.6.S690
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Published on 23 Oct 2007.
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