计算机工程与应用2011，Vol.47Issue(13)：161-165,5.DOI:10.3778/j.issn.1002-8331.2011.13.046
挖掘最大频繁项集的改进蚁群算法
Modified ant colony optimization for mining maximal frequent itemsets.
黄红星 1王秀丽 1黄习培1
作者信息
- 1. 福建农林大学,计算机与信息学院,福州,350002
- 折叠
摘要
Abstract
Mining Maximal Frequent Itemsets(MFI) is to find a maximal subset that appears frequently in datasets. There are many algorithms to effectively solve MFI.Ant Colony Optimization(ACO) is a new method to solve MFI.However,there are two bottlenecks: The ACO algorithm takes too much time and solves imprecisely for MFI.A novel ACO algorithm with max-min ant system and association graph is proposed. The tour graph is constructed. Ant colony constructs local maximal frequent itemsets under the instruction of dynamic pheromone and heuristic factor. It discovers global maximal frequent itemsets by new local and global update mechanism. Compared experiments show that this algorithm is fast and effective.关键词
数据挖掘/最大频繁项集/蚁群优化/最大最小蚂蚁系统/关联图Key words
date mining/maximum frequent itemsets/ant colony optimization/max-min ant system/association graph分类
信息技术与安全科学引用本文复制引用
黄红星,王秀丽,黄习培..挖掘最大频繁项集的改进蚁群算法[J].计算机工程与应用,2011,47(13):161-165,5.
