Mining Top-k Closed Co-location Patterns
In this paper, we present a problem to discover compact co-location patterns without minimum prevalence threshold. Aspatiao co-location is a set of spatial events being frequently observed together in nearby geographic space. Acommon frame-work for mining spatial co-location patterns employs a level-wised search method(like Apriori)to discover co-location patterns, and generates numerous redundant patterns since all of the 2l subsets of each length l event set the algorithms discover are included in the result set.In addition,most works of spatial co-location mining require the specification of a minimum prevalent threshold to find interesting co-location patterns. However, it is difficult for users to decide an appropriate threshold value without prior knowledge of their task-specific spatial data. Tosolve these problems, we propose a problem to min top-k closed co-location patterns, where k is the desired number of patterns, and develop an algorithm to efficiently find the interesting patterns. The experiment result shows that the proposed algorithm is effective in computation.
jin Soung Yoo Mark Bow
Computer Science Department Indiana University-purdue University Fort Wayne,IN,USA 46805
国际会议
福州
英文
100-105
2011-06-29（万方平台首次上网日期，不代表论文的发表时间）