多重最小支持度频繁项集挖掘算法研究

某些情况下提取关联规则挖掘时需要根据项目的特点设置不同的最小支持度，针对此问题进行了多重最小支持度的频繁项集挖掘算法研究。在FP-growth的基础上提出了多重最小支持度树(MS-tree)的新方法，并设计了MS-growth算法对MS-tree进行频繁模式集的挖掘。该算法只需扫描一次数据库，克服了MSapriori算法在生成关联规则时需要重新扫描数据库的缺点。实验表明，新算法的性能可以和FP-growth算法相比，而且可以处理多重最小支持度的问题。     

基于命题逻辑的关联规则挖掘算法L-Eclat

关联规则挖掘是数据挖掘领域非常重要的课题,在很多领域被广泛应用。关联规则挖掘算法都需要设置最小支持度和最小置信度。很多国内外学者研究的挖掘算法在这两方面都存在着一些问题,不仅需要大量的领域知识来设置合适的最小支持度,而且其结果集庞大、用户不容易理解。针对关联规则挖掘算法存在的问题,将命题逻辑融合到关联规则算法Eclat中,设计出了基于命题逻辑思想的挖掘算法L-Eclat。实验结果表明,L-Eclat算法压缩了挖掘的规则集,减小了算法的时间消耗,且即使是非常小的支持度也可以得到高质量的关联规则,这在一定程度上解决了支持度设置的问题。     

基于分段函数的多支持度关联规则挖掘算法

在多支持度关联规则挖掘算法中,针对最小支持度的选取问题,提出一种基于分段函数的多支持度关联规则挖掘算法.在多支持度算法中挖掘频繁集的时候,最小支持度由项集最小项支持度的最小值、最大值和给定的参考值所决定,这样避免了采用最小值作为最小支持度算法的时间复杂度高和存在无效规则的问题,以及采用最大值致使剪枝程度过大而造成规则遗漏的问题.通过实验结果表明了该算法的有效性.     

分布式数据库中关系数据正负关联规则挖掘

针对传统分布式数据库中关系数据正负关联规则挖掘的准确度较低、挖掘效率较低等问题,提出一种新的分布式数据库中关系数据正负关联规则挖掘方法.在关联规则基本概念和性质分析基础上,利用多级支持度从频繁项集中生成正关联规则,结合根据频繁项集和非频繁项集生成负关联规则,通过最小支持度合理设置相关置信度,引入不同权重值于各数据库中,实现分布式数据库中关系数据正负关联规则的挖掘.仿真结果表明,以上算法可有效识别结果规则集中的负关联规则和弱关联规则,确保数据库中关联数据挖掘更加准确;在不同最小支持度或不同事务数条件下,挖掘速度较快,提升了挖掘效率.     

基于中智模糊关联规则生成的大数据挖掘分析算法

关联规则挖掘作为一种大数据挖掘方法,被用于确定不同项目间存在的内在联系,并以大于某一阈值作为评判不同项间存在关联的依据。传统关联规则挖掘方法仅能建立不同项间的布尔型关联规则,存在硬化数据"尖锐边界"问题导致关联规则挖掘性能下降的缺点。为克服经典数据挖掘算法的这一缺陷,提出一种新型中智关联规则挖掘算法。基于专家知识对语言学术语进行量化预处理,得到不同指标的量化数据资料库;基于关联规则支持度定义计算不同指标项集合的支持度;通过考虑不同项间的隶属度、不确定度和非隶属度函数生成关联规则。将该中智挖掘算法与模糊挖掘算法进行对比,结果表明,该算法能够增加生成关联规则数量,有助于提高数据挖掘的准确性。     

直觉模糊关联规则挖掘及算法研究

针对关联规则挖掘中连续属性离散化时的"尖锐边界"问题,提出了一种用直觉模糊集合理论来改进关联规则挖掘的方法,定义了直觉模糊非支持度和非置信度的概念,阐述了"支持度-非支持度-置信度-非置信度"的关联规则挖掘度量机制.描述了直觉模糊关联规则挖掘的基本原理和算法,并给出了算法的基本步骤,最后用实例验证了此算法的有效性.     

基于FP-树的无支持度设置关联规则挖掘

关联规则的挖掘大都在用户设置的支持度阈值的限制条件下，产生频繁项目集合。得到关联规则。但是没有相关领域的专门知识，用户很难设置合适的支持度闽值来得到合适的结果。文章在FP-树挖掘算法的基础上，提出一种无支持度设置的关联规则挖掘方法，通过兴趣度的设置动态地得到不同项集的不同支持度。     

多段支持度数据频繁模式关联规则挖掘仿真

针对传统数据关联挖掘过程只适用于单段数据集,导致内存负担重、挖掘频繁项集效率不高等问题,提出一种多段支持度数据频繁模式关联规则挖掘方法.运用多支持度算法对数据集逐步搜索,数据集按照数据项的MIS大小有序排列,采用最小值作为最小支持度,确保该算法的地推性.构建FP_ tree树,利用FP_tree算法对待选项实施剪枝,从而准确挖掘出频繁模式的关联规则.仿真结果证明,多段支持度数据频繁模式关联规则挖掘具有较好的性能,有效提高了关联规则的挖掘效率.     

最大值控制的多最小支持度关联规则挖掘算法

大部分关联规则挖掘算法使用同一最小支持度阈值进行挖掘，但在实际使用中由干各项目发生频率的不同，理应有不同的最小支持度支持。该文提出了一种多最小支持度关联规则挖掘算法，为每一项目设置一最小支持度，同时在生成舒选集和最大频繁集的过程中使用最大值控制来实现剪枝，有效地提高了该算法的效率，最后用一个超市销售物品的例子来说明该算法的使用。     

基于兴趣度的关联规则挖掘

关联规则挖掘是数据挖掘领域中的重要研究内容之一。然而,传统的基于支持度-可信度框架的挖掘方法可能会产生大量不相关、甚至是误导的关联规则。针对现有关联规则挖掘的评价标准存在的问题,提出在评价标准中增加兴趣度,并给出了兴趣度的定义和基于兴趣度的关联规则挖掘算法。利用兴趣度将关联规则分为正关联规则和负关联规则,从而可以用算法挖掘带有负项的关联规则。实验结果分析表明,在传统挖掘方法的基础上引入兴趣度,可以有效地减少正关联规则的规模,产生有意义的负关联规则。     

Mining high utility itemsets by dynamically pruning the tree structure

Mining high utility itemsets is one of the most important research issues in data mining owing to its ability to consider nonbinary frequency values of items in transactions and different profit values for each item. Mining such itemsets from a transaction database involves finding those itemsets with utility above a user-specified threshold. In this paper, we propose an efficient concurrent algorithm, called CHUI-Mine (Concurrent High Utility Itemsets Mine), for mining high utility itemsets by dynamically pruning the tree structure. A tree structure, called the CHUI-Tree, is introduced to capture the important utility information of the candidate itemsets. By recording changes in support counts of candidate high utility items during the tree construction process, we implement dynamic CHUI-Tree pruning, and discuss the rationality thereof. The CHUI-Mine algorithm makes use of a concurrent strategy, enabling the simultaneous construction of a CHUI-Tree and the discovery of high utility itemsets. Our algorithm reduces the problem of huge memory usage for tree construction and traversal in tree-based algorithms for mining high utility itemsets. Extensive experimental results show that the CHUI-Mine algorithm is both efficient and scalable.     

Updating high-utility pattern trees with transaction modification

Traditional association-rule mining only concerns the occurrence frequencies of the items in a binary database. In real-world applications, customers may buy several copies of the purchased items. Other factors such as profit, quantity, or price should be concerned to measure the utilities of the purchased items. High-utility itemsets mining was thus proposed to consider the factors of quantity and profit. Two-phase model was the most commonly way to keep the transaction-weighted utilization downward closure property, thus reducing the numerous candidates in utility mining. Most methods for finding high-utility itemsets are used to handle a static database. In practical applications, transactions are changed whether insertion, deletion, or modification. Some itemsets may arise as the new high-utility itemsets or become invalid knowledge in the updated database. In this paper, a maintenance Fast Updated High Utility Pattern tree for transaction MODification (FUP-HUP-tree-MOD) algorithm is thus proposed to effective maintain and update the built HUP tree for mining high-utility itemsets in dynamic databases without candidate generation. Experiments are conducted to show better performance of the proposed algorithm compared to the two-phase algorithm and the HUP tree algorithm in batch mode.     

Efficient updating of discovered high-utility itemsets for transaction deletion in dynamic databases

Most algorithms related to association rule mining are designed to discover frequent itemsets from a binary database. Other factors such as profit, cost, or quantity are not concerned in binary databases. Utility mining was thus proposed to measure the utility values of purchased items for finding high-utility itemsets from a static database. In real-world applications, transactions are changed whether insertion or deletion in a dynamic database. An existing maintenance approach for handling high-utility itemsets in dynamic databases with transaction deletion must rescan the database when necessary. In this paper, an efficient algorithm, called PRE-HUI-DEL, for updating high-utility itemsets based on the pre-large concept for transaction deletion is proposed. The pre-large concept is used to partition transaction-weighted utilization itemsets into three sets with nine cases according to whether they have large (high), pre-large, or small transaction-weighted utilization in the original database and in the deleted transactions. Specific procedures are then applied to each case for maintaining and updating the discovered high-utility itemsets. Experimental results show that the proposed PRE-HUI-DEL algorithm outperforms a batch two-phase algorithm and a FUP2-based algorithm in maintaining high-utility itemsets.     

Effective utility mining with the measure of average utility

Frequent-itemset mining only considers the frequency of occurrence of the items but does not reflect any other factors, such as price or profit. Utility mining is an extension of frequent-itemset mining, considering cost, profit or other measures from user preference. Traditionally, the utility of an itemset is the summation of the utilities of the itemset in all the transactions regardless of its length. The average utility measure is thus adopted in this paper to reveal a better utility effect of combining several items than the original utility measure. It is defined as the total utility of an itemset divided by its number of items within it. The average-utility itemsets, as well as the original utility itemsets, does not have the “downward-closure” property. A mining algorithm is then proposed to efficiently find the high average-utility itemsets. It uses the summation of the maximal utility among the items in each transaction with the target itemset as the upper bound to overestimate the actual average utilities of the itemset and processes it in two phases. As expected, the mined high average-utility itemsets in the proposed way will be fewer than the high utility itemsets under the same threshold. The proposed approach can thus be executed under a larger threshold than the original, thus with a more significant and relevant criterion. Experimental results also show the performance of the proposed algorithm.     

频繁和高效用项集挖掘

对从事务数据库中挖掘有意义的项集的研究已超过10年.然而,大多数的研究要么使用频繁度或支持度(如频繁项集挖掘),要么使用效用值或利润(如高效用项集挖掘)作为主要的衡量标准.单独使用这两种衡量方式都有各自的局限性,比如频繁度很高的项集其效用值有可能很低,而效用值很高的项集其频繁度往往很低,将这些项集推荐给用户没有意义.将这两种衡量标准综合考虑,希望找出那些频繁度和效用值都很高的项集.该项工作最大的挑战是效用值既不满足单调性也不满足反单调性.因此,提出了高效算法FHIMA.FHIMA采用PrefixSpan的思想,挖掘时能避免产生非频繁的候选项集.此外,还根据效用和质量上界的一些性质,有效地缩小了搜索空间,极大地提高了FHIMA算法的效率.     

改进的基于频繁模式树的最大频繁项集挖掘算法——FP-MFIA

针对最大频繁项目集挖掘算法(DMFIA)当候选项目集维数高而最大频繁项目集维数较低的情况下要产生大量的候选项目集的缺点,提出了一种改进的基于频繁模式树(FP-tree)结构的最大频繁项目集挖掘算法--FP-MFIA。该算法根据FP-tree的项目头表,采用自底向上的搜索策略逐层挖掘最大频繁项目集,从而加速每次对候选集计数的操作。在挖掘时根据每层的条件模式基产生维数较低的非频繁项目集,尽早对候选项目集进行剪枝和降维,可大量减少候选项目集的数量。同时在挖掘时充分利用最大频繁项集的性质,减少搜索空间。通过算法在不同支持度下挖掘时间的对比可知,算法FP-MFIA在最小支持度较低的情况下时间效率是DMFIA以及基于降维的最大频繁模式挖掘算法(BDRFI)的2倍以上,说明FP-MFIA在候选集维数较高的时候优势明显。     

Actionable high-coherent-utility fuzzy itemset mining

Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from quantitative transaction databases. Since each item has its own utility, utility itemset mining has become increasingly important. However, common problems with existing approaches are that an appropriate minimum support is difficult to determine and that the derived rules usually expose common-sense knowledge, which may not be interesting from a business point of view. This study thus proposes an algorithm for mining high-coherent-utility fuzzy itemsets to overcome problems with the properties of propositional logic. Quantitative transactions are first transformed into fuzzy sets. Then, the utility of each fuzzy itemset is calculated according to the given external utility table. If the value is larger than or equal to the minimum utility ratio, the itemset is considered as a high-utility fuzzy itemset. Finally, contingency tables are calculated and used for checking whether a high-utility fuzzy itemset satisfies four criteria. If so, it is a high-coherent-utility fuzzy itemset. Experiments on the foodmart and simulated datasets are made to show that the derived itemsets by the proposed algorithm not only can reach better profit than selling them separately, but also can provide fewer but more useful utility itemsets for decision-makers.     

矩阵与前缀树方法挖掘频繁项集

传统频繁项集挖掘算法的执行效率较低。提出了一种基于矩阵与前缀树的频繁项集挖掘算法MPFI,能快速地挖掘事务数据库中的频繁项集。MPFI算法只需扫描事务数据库一次,构建垂直方向的二进制矩阵,应用二进制位向量表达频繁项集信息,利用前缀树压缩存储频繁项集的相关信息,不产生候选项集。理论分析与实验结果表明,MPFI算法能有效地提高频繁项集挖掘效率。     

一种不产生候选项挖掘频繁项集的新算法

Apriori算法是关联规则挖掘算法中应用最为广泛的一种算法,它的主要目的是从大量的事务数据中通过候选项集挖掘出有趣的频繁项集,从而为用户提供有意义的关联关系。但随着数据库规模的扩大,apriori算法可能会产生如下两大棘手问题:大量候选项集的产生将造成巨大计算量的浪费;为剪掉无用候选项如何设置阈值。这些问题相对于众多普通用户来说都具有挑战性。该文提出的代码与运算是一种无须候选项挖掘频繁项集的算法,用户无须为设置阈值而煞费苦心。同时事务压缩算法的加入大大减少了算法中的计算量。