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Search Results: 1 - 10 of 1399 matches for " 黄名选 "
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基于项权值变化的完全加权正负关联规则挖掘
周秀梅,黄名
电子学报 , 2015, DOI: 10.3969/j.issn.0372-2112.2015.08.012
Abstract: 本文提出一种基于项权值变化的完全加权正负关联规则挖掘算法,解决了基于项权值变化的负模式挖掘问题.该算法考虑项权值依赖于事务记录的特点,采用新的项集剪枝方法和模式评价框架,通过项集的项内权值比和维数比的简单计算和比较,挖掘有效的完全加权正负关联规则.实验结果表明,与现有无加权正负关联规则挖掘算法比较,本文算法能避免无效的模式出现,其挖掘时间和候选项集数量明显减少,减幅最大分别可达94.09%和88.16%.
基于apriori改进算法的局部反馈查询扩展
陈燕红,黄名
现代图书情报技术 , 2007,
Abstract: ?提出面向查询扩展的apriori改进算法,采用三种剪枝策略,极大提高挖掘效率;针对现有查询扩展存在的缺陷,提出基于apriori改进算法的局部反馈查询扩展算法,该算法用apriori改进算法对前列初检文档进行词间关联规则挖掘,提取含有原查询词的词间关联规则,构造规则库,从库中提取扩展词,实现查询扩展。实验结果表明该算法能够提高信息检索性能,与现有算法比较,在相同查全率水平级下其平均查准率有了明显提高。
一种基于词间关联规则挖掘的查询扩展方法
黄名,黄发良
图书情报工作 , 2008,
Abstract: ?针对现有信息检索系统中存在的词不匹配问题,提出一种基于词间关联规则的查询扩展算法,该算法利用现有挖掘算法自动对前列初检文档进行词间关联挖掘,提取含有原查询词的词间关联规则,从中提取扩展词,实现查询扩展。实验结果表明,该算法能改善和提高信息检索系统的查全率和查准率,具有很高的应用价值,与未进行查询扩展时相比,采用本文查询扩展算法后,平均准确率提高了13.34%,与传统的局部上下文分析查询扩展算法比较,其平均准确率提高了4.87%。
有效的矩阵加权正负关联规则挖掘算法——mwarm-srccci
周秀梅,黄名
计算机应用 , 2014,
Abstract: ?针对现有加权关联规则挖掘算法不能适用于矩阵加权数据的缺陷,给出一种新的矩阵加权项集剪枝策略,构建矩阵加权正负关联模式评价框架srccci,提出一种新的基于srccci评价框架的矩阵加权正负关联规则挖掘算法mwarm-srccci。该算法克服了现有挖掘技术的缺陷,采用新的剪枝技术和模式评价方法,挖掘有效的矩阵加权正负关联规则,避免一些无效和无趣的模式产生。以中文web测试集cwt200g为实验数据,与现有无加权正负关联规则挖掘算法比较,mwarm-srccci算法的挖掘时间减幅最大可达74.74%。理论分析和实验结果表明,mwarm-srccci算法具有较好的剪枝效果,候选项集数量和挖掘时间明显减少,挖掘效率得到极大提高,其关联模式可为信息检索提供可靠的查询扩展词来源。
Query Expansion of Local Feedback Based on Improved Apriori Algorithm
基于Apriori改进算法的局部反馈查询扩展

Chen Yanhong,Huang Mingxuan,
陈燕红
,黄名

现代图书情报技术 , 2007,
Abstract: An improved Apriori algorithm for query expansion is presented based on the thrice pruning strategy.This method can tremendously enhance the mining efficiency.After studying the limitations of existing query expansion,a novel query expansion algorithm of local feedback is proposed based on the improved Apriori algorithm.This algorithm can automatically mine those association rules related to original query in the top-rank retrieved documents using the improved Apriori algorithm,to construct an association rules-based database,and extract expansion terms related to original query from the database for query expansion. Experimental results show that our method is better than traditional ones in average precision.
Query Expansion of Pseudo Relevance Feedback Based on Feature Terms Extraction and Correlation Fusion
特征词抽取和相关性融合的伪相关反馈查询扩展

Feng Ping Huang Mingxuan,
冯平
,黄名

现代图书情报技术 , 2011,
Abstract: Aiming at the term mismatch issues of existing information retrieval systems, a novel query expansion algorithm of pseudo relevance feedback is proposed based on feature terms extraction and correlation fusion. At the same time, a new computing method for weights of expansion terms is also given. The algorithm can extract feature terms related to original query from the n chapter top-ranked retrieved local documents, and then identify those feature terms as final expansion terms according to the frequency of each feature term appeared in the local documents and the correlation between each feature term and the entire original query for query expansion. The results of the experiment show that the method is effective,and it can enhance and improve the performance of information retrieval.
基于矩阵加权关联规则挖掘的伪相关反馈查询扩展
黄名,严小卫?,张师超?
软件学报 , 2009,
Abstract: 提出一种面向查询扩展的矩阵加权关联规则挖掘算法,给出与其相关的定理及其证明过程.该算法采用4种剪枝策略,挖掘效率得到极大提高.实验结果表明,其挖掘时间比原来的平均时间减少87.84%.针对现有查询扩展的缺陷,将矩阵加权关联规则挖掘技术应用于查询扩展,提出新的查询扩展模型和更合理的扩展词权重计算方法.在此基础上提出一种伪相关反馈查询扩展算法——基于矩阵加权关联规则挖掘的伪相关反馈查询扩展算法,该算法能够自动地从前列n篇初检文档中挖掘与原查询相关的矩阵加权关联规则,构建规则库,从中提取与原查询相关的扩展词,实现查询扩展.实验结果表明,该算法的检索性能确实得到了很好的改善.与现有查询扩展算法相比,在相同的查全率水平级下,其平均查准率有了明显的提高.
Study on Query Expansion Model Based on Association Rules Mining
基于关联规则挖掘的查询扩展模型研究

Huang Mingxuan Chen Yanhong Zhang Shichao,
黄名
,陈燕红,张师超

现代图书情报技术 , 2007,
Abstract: In order to better apply association rule mining technique to query expansion and find out some better query expansion models,4 categories of query expansion models with 13 varieties are given based on item-all-weighted association rule mining.Comparison of retrieval performances are made through experiments.Some better query expansion models are discovered.
Efficient model for information retrieval
一种有效的信息检索模型*

HUANG Ming-xuan,ZHANG Shi-chao,
黄名
,张师超

计算机应用研究 , 2008,
Abstract: This paper introduced a novel and efficient information retrieval model based on users' query behaviors and query expansion.Expounded its design ideology and algorithm.The results of the experiment proposed model improves more precision than the traditional information retrieval methods.
基于项权值变化和SCCI框架的加权正负关联规则挖掘
黄名,黄发良,严小卫,兰慧红
控制与决策 , 2015, DOI: 10.13195/j.kzyjc.2014.1102
Abstract: 给出项权值变化的数据模型形式化表示,构建新的加权项集剪枝策略及其模式评价框架SCCI(supportconfidence-correlation-interest),提出基于项权值变化和SCCI评价框架的加权正负关联规则挖掘算法.该算法考虑了项权值变化的数据特点,采用新的剪枝方法和评价框架,通过项集权值简单计算和比较,挖掘有效的加权正负关联规则.实验结果表明,该算法能够有效地减少候选项集数量和挖掘时间,挖掘出有趣的关联模式,避免无效模式出现,挖掘效率高于相比较的现有算法,解决了项权值变化的加权负模式挖掘问题.
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