一种模糊对象的极大co-location模式挖掘算法  

Algorithm of Mining Maximal Co-location Patterns for Fuzzy Objects

作　　者：温佛生 肖清[1] 王丽珍[1] 孔兵[1] 

机构地区：[1]云南大学信息学院,昆明650091

出　　处：《计算机科学》2014年第1期138-145,共8页Computer Science

基　　金：国家自然科学基金资助项目(61063008;61272126;61262069);云南省应用基础研究基金项目(2010CD025);云南省教育厅基金项目(2012C103)资助

摘　　要：空间co-location模式表示的是空间对象的实例在一个相同的区域内频繁地进行空间并置。人们已经对确定和不确定数据co-location模式挖掘做了很多工作,也有很多成果,但对极大co-location模式挖掘研究较少,特别是针对模糊对象的极大co-location模式挖掘研究还未见报道。提出Mevent-tree算法来挖掘模糊对象的极大co-location模式,首先为每个对象构建空间对象树,从而得到候选模式,然后为候选模式集构建HUT树,最后在HUT树中从阶数最大的候选模式开始到阶数2为止,深度优先搜索极大co-location模式并在得到极大模式后对HUT树剪枝。接着提出两个改进算法,包括预处理阶段模糊对象的剪枝算法和在构造HUT树之前co-location候选模式的剪枝算法。最后通过大量实验验证了Mevent-tree算法和改进算法的效果和效率。A spatial co-location pattern is a group of spatial objects whose instances are frequently located m me same region. There are lots of jobs and achievements of co-location patterns mining algorithrns for certain and uncertain data, but less maximal co-location patterns mining algorithms, especially spatial maximal co-location patterns for fuzzy ob- jects. Mevent-tree algorithm was proposed in this paper for mining maximal co-location patterns for fuzzy ohiects. First- ly,it builds a event tree which can get candidate patterns for each object, build the HUT tree of candidate patterns, and then depth-first searches maximal co-location patterns begining with maximal-size candidate patterns and ending to size- 2 candidate patterns in HUT tree, as well pruning co-location candidate patterns after geting maximal co-location pat-terns. Then we put forward two improved strategies, including the pruning fuzzy objects during preprocessing and the pruning co-location candidates before creating HUT trees. Finally, extensive experiments show the effectiveness and efficiency of Mevent-tree algorithm and its improved methods.

关 键 词：模糊对象 极大co-location模式挖掘 模糊参与率 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]