The Application of a Top-Down Algorithm in Neighboring Class Set Mining

摘要：

This paper focuses on character of present frequent neighboring class set mining algorithms which is suitable for mining short frequent neighboring class set, and introduces a top-down algorithm in frequent neighboring class set mining. This algorithm is suitable for mining long frequent neighboring class set in large spatial data according to top-down strategy, and it creates digital database of neighboring class set via neighboring class bit sequence. The algorithm generates candidate frequent neighboring class set via top-down search strategy, namely, it gains k-neighboring class set as candidate frequent items by computing k-subset of (k+1)-non frequent neighboring class set. The mining algorithm computes support of candidate frequent neighboring class set by digit logical operation. The algorithm improves mining efficiency through these two methods. The result of experiment indicates that the algorithm is faster and more efficient than present algorithms when mining long frequent neighboring class set in large spatial data.

作者: Gang FANG Cheng-Sheng TU Jiang XIONG Zi-Quan WANG

作者单位: College of Math and Computer Science Chongqing Three Gorges University Chongqing 404000, P.R.China

会议类型: 国际会议

会议名称: The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 234-237

在线出版日期: 2010-11-15（万方平台首次上网日期，不代表论文的发表时间）