计算机工程2012，Vol.38Issue(2)：72-74,3.DOI:10.3969/j.issn.1000-3428.2012.02.023
基于层次梯度分析的协同数据挖掘算法
Collaborative Data Mining Algorithm Based on Level Grads Analysis
摘要
Abstract
The classic data mining algorithm produces a lot of frequent-item set, which is not applied to the massive data mining in Intelligent Transportation System(ITS). This paper proposes an algorithm based on level grads without candidate items analysis that is used for computing association rules under the heterogeneous environment. It uses the concept of both level grads and mining topic transaction databases forming the level transaction database and mining the local frequent-item. The main-node uses the concept of weakly-entropy to abstract some association rules. Simulation results show that this algorithm has better performance in collaborative mining without candidate support.关键词
协同数据挖掘/关联规则/层次梯度/层次业务数据库Key words
collaborative data raining/association rule/level grads/level transaction database分类
信息技术与安全科学引用本文复制引用
潘冬生,章昭辉,代秀娟,杨娟..基于层次梯度分析的协同数据挖掘算法[J].计算机工程,2012,38(2):72-74,3.基金项目
安徽高校省级自然科学研究基金资助重点项目(KJ2008A104,KJ2009A096) （KJ2008A104,KJ2009A096）
芜湖市2010年度科技计划基金资助项目 （）
