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A novel frequent patterns mining method of unusual climate events in data of East Asian monsoon zone

机译:一种新型频繁模式挖掘在东亚季风区数据中异常气候事件的挖掘方法

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Unusual climate events which may cause disasters have great influence on both the natural environment and the human society. Finding association patterns among these events has great significance. Traditional data mining methods have several problems while applied to climate science data directly so we propose a novel method that mining frequent patterns among unusual events in climatic data, including spatial clustering algorithm based on tight clique, extracting unusual climate events algorithm and extended generalized sequential pattern (EGSP) algorithm. In order to verify our method, we do experiments on real climatic data (Climatic data of East Asian monsoon zone) and find lots of well-known and previously unknown patterns. It needs the climatic expert to judge whether the new patterns are significative. Overall, the experiment told us it was an effective and viable method for the climatic research.
机译:可能导致灾害可能导致灾害的不寻常的气候事件对自然环境和人类社会产生很大影响。在这些事件中寻找关联模式具有重要意义。传统的数据挖掘方法直接应用于气候科学数据,因此我们提出了一种新颖的方法,即在气候数据中挖掘不寻常事件中的频繁模式,包括基于紧密集团的空间聚类算法,提取异常的气候事件算法和扩展广义连续模式(EGSP)算法。为了验证我们的方法,我们对实际气候数据进行实验(东亚季风区的气候数据),并找到许多众所周知和以前未知的模式。它需要对气候专家来判断新模式是否具有重要意义。总体而言,实验告诉我们,这是一种对气候研究的有效和可行的方法。

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