摘要
为降低经典信息熵属性约简算法的时间复杂度,在论证信息熵属性约简与论域划分细化约简等价的基础上,提出将蚁群并行优化处理机制引入划分细化约简过程,在蚁群搜索过程中使用体现属性约简特点的状态转移和信息素更新策略.通过复杂性分析和实例验证,新算法可有效避免蚁群搜索的盲目性,并在较小迭代规模下快速获得约简集,更适于大容量数据表的处理.
To decrease the time complexity of the attribute reduction algorithm based on information entropy, a concurrent processing of ants colony optimization is introduced into partition based on the equivalence of attribute reduction between partition and information entropy. In the ant searching process the strategies of state transfer and pheromone update reflect the characteristics of the attribute reduction. Analysis of this algorithm＇ s complexity and simulation show that the blindness of the ant searching can be avoided and the size of the iteration is reduced to make the reduction faster. For large data base, it is more suitable.
出处
《北京邮电大学学报》
EI
CAS
CSCD
北大核心
2011年第6期55-58,共4页
Journal of Beijing University of Posts and Telecommunications
基金
国家高技术研究发展计划项目(2009AA04Z136)
关键词
数据挖掘
蚁群优化
等价划分
属性约简
data mining
ant colony optimization
partition
attribute reduction
