MINING TOP-RANK-K FREQUENT PATTERNS

摘要：

There have been many studies on efficient discovery of frequent patterns in large databases.The usual framework is to use a minimal support threshold to obtain all frequent patterns.However, it is nontrivial for users to choose a suitable minimal support threshold.In this paper, a new mining task called mining top-rank-k frequent patterns, where k is the biggest rank value of all frequent patterns to be mined, has been proposed.After deep analyzing the properties of top-rank-k frequent patterns, we propose an efficient algorithm called FAE to mining top-rank-k frequent patterns.FAE is the abbreviation of Filtering and Extending.During the mining process of FAE, the undesired patterns are filtered and useful patterns are selected to generate other longer potential frequent patterns.This strategy greatly reduces the search space.We also present results of applying these algorithms to a synthetic data set, which show the effectiveness of our algorithms.

关键词： Data mining Pattern mining Frequent patterns

作者: ZHI-HONG DENG GUO-DONG FANG

作者单位: State Key Laboratory of Machine Perception, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 851-856

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）