Show simple item record

dc.contributor.author胡慧蓉
dc.contributor.author王周敬
dc.date.accessioned2017-11-14T01:29:22Z
dc.date.available2017-11-14T01:29:22Z
dc.date.issued2005-07-10
dc.identifier.citation计算机应用,2005,(07):107-109
dc.identifier.issn1001-9081
dc.identifier.otherJSJY200507032
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/142446
dc.description.abstract首先对关联规则挖掘问题进行了简单的回顾,然后应用关系理论思想,引入了项目可辨识向量及其“与”运算,设计了一种快速挖掘算法SLIG,将频繁项目集的产生过程转化为项目集的关系矩阵中向量运算过程。算法只需扫描一遍数据库,克服了Apriori及其相关算法产生大量候选集和需多次扫描数据库的缺点。实验证明,与Apriori算法相比,SLIG算法提高了挖掘效率。
dc.description.abstractAfter the introduction of famous Apriori algorithms, an efficient algorithm SLIG(Single-level Large Itemsets Generation) learning from relation theory and "AND" operation on recognizable vectors was proposed. SLIG transformed the production process of frequent itemset to the vector calculation process in relationship matrix and only needed to scan the database once. Empirical evaluation and experiments show that SLIG is more efficient than the algorithms that need to pass the large database many times.
dc.language.isozh_CN
dc.subject关联规则
dc.subject频繁集
dc.subject可辨识向量
dc.subject可辨识矩阵
dc.subjectassociation rule
dc.subjectfrequent itemset
dc.subjectrecognizable vectors
dc.subjectrecognizable matrix
dc.title一种基于关系矩阵的关联规则快速挖掘算法
dc.title.alternativeFast algorithm for mining association rules based on relationship matrix
dc.typeArticle


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record