空间co-location模式挖掘算法介绍及应用

当前挖掘空间co-location模式所遇到的困难在于,空间对象的实例分布在连续的空间中并拥有复杂的空间关系,大部分的计算时间需要用来生成co-location模式的表实例.分析了co-location模式挖掘的实质,以及近年来提出的co-location模式挖掘的全连接算法和无连接算法,并对这两种算法在性能上加以比较.在此基础上,结合三江并流国家基金项目,用这两种算法挖掘出了共生植被及其分布情况,为生物学家的科学研究提供了有利的帮助.

Introduction and Application to Spatial Co-location Patterns Mining Algorithm

Co-location pattern discovery presents challenges since the instances of spatial features are embedded in a continuous space and share a variety of spatial relationships.A large fraction of the computation time is devoted to identifying the instances of co-location patterns.The co-location mining problems are analyzed,and two algorithms of mining co-location patterns are discussed.They are full-join algorithm and join-less algorithm.They are used in a plant distributing dataset of "Three Parallel Rivers" region to mining symbiotic plant species.It shows that the mining results are interested by botanist.