共查询到18条相似文献,搜索用时 171 毫秒
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《数字社区&智能家居》2008,(Z2)
本文基于遗传算法的思想,并结合关联规则挖掘的要求与特点,提出了一个基于遗传算法的关联规则挖掘方法,通过实例,分析给出了详细的利用遗传算法挖掘关联规则的实现方法,并提出双层循环结构,利用基因重组、一致变异以及自适应参数的手段调整遗传算法进行数据挖掘,以此证明利用这个模型来发现关联规则是可行的、有效的.最后指出遗传算法的特点和基于遗传算法的关联规则挖掘技术的前景. 相似文献
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数据挖掘是关联规则中一个重要的研究方向。该文对关联规则的数据挖掘和遗传算法进行了概述,提出了一种改进型遗传算法的关联规则提取算法。最后结合实例给出了用遗传算法进行关联规则的挖掘方法。 相似文献
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数据挖掘是关联规则中一个重要的研究方向。该文对关联规则的数据挖掘和遗传算法进行了概述,提出了一种改进型遗传算法的关联规则提取算法。最后结合实例给出了用遗传算法进行关联规则的挖掘方法。 相似文献
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首先对关联规则的数据挖掘和遗传算法进行了介绍,根据关联规则的要求和特点,结合遗传算法的思想。提出了一种基于遗传算法的关联规则挖掘算法,并给出一简单实例,说明本文方法的有效性。 相似文献
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关联规则挖掘与分类规则挖掘的比较研究 总被引:1,自引:0,他引:1
关联规则挖掘与分类规则挖掘都是数据挖掘,领域中很重要的技术。本文首先简要介绍了关联规则挖掘和分类规则挖掘的基本知识,主要从挖掘目的、发现规则算法的方法、算法的设计思想等几个方面对它们进行了比较,最后介绍了它们之间的联系。 相似文献
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一种新的多维关联规则挖掘算法 总被引:12,自引:0,他引:12
关联规则是数据挖掘中一个重要课题.文章给出一种基于遗传算法和蚂蚁算法相结合的多维关联规则挖掘算法.新算法利用了遗传和蚂蚁算法共有的良好全局搜索能力,并克服了遗传算法局部搜索能力弱和蚂蚁算法搜索速魔慢的缺陷.实验结果表明,新算法在对具有稀疏特性的多维关联规则的挖掘中体现了良好的性能. 相似文献
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基于免疫遗传算法的多维关联规则挖掘 总被引:7,自引:1,他引:7
高坚 《计算机工程与应用》2003,39(32):185-186,225
关联规则挖掘是数据挖掘中一个很重要的研究课题。文章给出了一种基于免疫遗传算法的关联规则挖掘算法,该算法具有很好的鲁棒性和隐含并行性,能快速、有效地进行全局优化搜索。特别适用于大规模、海量数据库的挖掘。 相似文献
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FrequentItemsetMining (FIM) is one of the most important data mining tasks and is the foundation of many data mining tasks. In Big Data era, centralized FIM algorithms cannot meet the needs of FIM for big data in terms of time and space, so Distributed Frequent Itemset Mining (DFIM) algorithms have been designed to meet the above challenges. In this paper, LocalGlobal and RedistributionMining which are two main paradigms of DFIM algorithm are discussed; Two algorithms of these paradigms on MapReduce named LG and RM are proposed while MapReduce is a popular distributed computing model, and also the related work is discussed. The experimental results show that the RM algorithm has better performance in terms of computation and scalability of sites, and can be used as the basis for designing the DFIM algorithm based on MapReduce. This paper also discusses the main ideas of improving the DFIM algorithms based on MapReduce. 相似文献
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数据挖掘在异常入侵检测系统中的应用 总被引:4,自引:0,他引:4
在分析现有入侵检测技术和系统的基础上,本文提出了一种基于数据挖掘和可滑动窗口的异常检测模型,该模型综合利用了关联规则和序列模式算法对网络数据进行充分挖掘,分别给出了基于时间窗口的训练阶段和检测阶段的挖掘算法,并建立贝叶斯网络,进一步判定规则挖掘中的可疑行为,提高检测的准确率。 相似文献
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关联规则挖掘研究述评 总被引:19,自引:0,他引:19
1 引言近年来,数据挖掘(又称为数据库中知识发现,KDD)引起了信息产业界的极大关注。关联规则挖掘作为数据挖掘的一种重要模式,已成为数据挖掘领域的一个非常重要的研究课题。它在商务管理、生产控制、市场分析、工程设计、科学探索等领域都有着重要的应用,目前又逐渐向生物医药、金融分析、电信等领域渗透。 相似文献
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To date, association rule mining has mainly focused on the discovery of frequent patterns. Nevertheless, it is often interesting to focus on those that do not frequently occur. Existing algorithms for mining this kind of infrequent patterns are mainly based on exhaustive search methods and can be applied only over categorical domains. In a previous work, the use of grammar-guided genetic programming for the discovery of frequent association rules was introduced, showing that this proposal was competitive in terms of scalability, expressiveness, flexibility and the ability to restrict the search space. The goal of this work is to demonstrate that this proposal is also appropriate for the discovery of rare association rules. This approach allows one to obtain solutions within specified time limits and does not require large amounts of memory, as current algorithms do. It also provides mechanisms to discard noise from the rare association rule set by applying four different and specific fitness functions, which are compared and studied in depth. Finally, this approach is compared with other existing algorithms for mining rare association rules, and an analysis of the mined rules is performed. As a result, this approach mines rare rules in a homogeneous and low execution time. The experimental study shows that this proposal obtains a small and accurate set of rules close to the size specified by the data miner. 相似文献