Journal of Software:2014.25(S2):189-200

(云南大学 信息学院 计算机科学与工程系, 云南 昆明 650091;云南民族大学 数学与计算机科学学院, 云南 昆明 650031)
Incremental Mining and Evolutional Analysis of Co-Locations
LU Jun-Li,WANG Li-Zhen,XIAO Qing,WANG Xin
(Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650091, China;School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650031, China)
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Received:May 07, 2014    Revised:August 19, 2014
> 中文摘要: 空间co-location模式挖掘是空间数据挖掘的一个重要研究方向.空间co-location模式是空间对象的一个子集,它们的实例在空间中频繁关联.到目前为止,空间co-location模式挖掘都只关注某一个时刻的空间co-location模式.然而,在实际应用中,数据库中的数据是随着时间改变的,所以高效地增量挖掘空间co-location模式是非常必要的;空间co-location模式演化分析可以发现空间co-location模式的变化规律,预测特定事件的发生,但是对这些问题的研究并未见诸报道.研究了高效的空间co-location模式增量挖掘及空间co-location模式的演化分析,首先,提出了高效的空间co-location模式增量挖掘基本算法及剪枝算法.其次,在多个随时间变化的真实数据集上挖掘co-location演化模式.再次,证明了空间co-location模式增量挖掘基本算法及剪枝算法是正确的和完备的.最后,在"模拟+真实"的数据集上用充分的实验验证了增量挖掘基本算法的性能以及剪枝算法的剪枝效果.此外,把空间co-location增量挖掘基本算法、剪枝算法及演化模式挖掘算法应用到三江并流区域珍稀植物数据集上,增量挖掘出空间co-location模式及演化模式,预测了co-location模式的演化规律,更好地实现了对珍稀植物的动态跟踪和保护.
Abstract:Spatial co-locations mining is an important research domain in spatial data mining. Spatial co-locations represent the subsets of spatial features which are frequently located together in geographic space. Up to present, all the existing co-location mining algorithms only focus on discovering ordinary co-location patterns or co-location rules. However, in real-world applications, the data in a database do not usually remain a stable condition, making efficient incremental mining for co-locations very indispensable and interesting. The evolutionary analysis of co-locations can discover the development rules of co-locations, and predict the particular event happened in future. However, no results have yet been reported from these researches. This paper studies the incremental mining for co-locations and the evolutionary analysis of co-locations. Firstly, an efficient basic algorithm and a prune algorithm for incremental mining are proposed. Secondly, evolutionary co-locations are discovered based on several real datasets. Thirdly, both the basic algorithm and prune algorithm are proved correct and complete. Fourth, extensive experiments are performed to verify the performance and effectiveness of the basic algorithm and prune algorithm. At last, the basic algorithm and prune algorithm for incremental mining in conjunction with the evolutionary co-locations mining algorithm are applied to the Three Parallel Rivers of Yunnan protected Areas plant database to predict the development rules of co-locations, and dynamically track and protect the rare plants of this area.
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基金项目:国家自然科学基金(61472346,61272126);云南省教育厅基金(2012C103) 国家自然科学基金(61472346,61272126);云南省教育厅基金(2012C103)
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LU Jun-Li,WANG Li-Zhen,XIAO Qing,WANG Xin.Incremental Mining and Evolutional Analysis of Co-Locations.Journal of Software,2014,25(S2):189-200