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1.
Most methods for mining association rules from tabular data mine simple rules which only use the equality operator “=” in their items. For quantitative attributes, approaches tend to discretize domain values by partitioning them into intervals. Limiting the operator only to “=” results in many interesting frequent patterns that may not be identified. It is obvious that where there is an order between objects, operators such as greater than or less than a given value are as important as the equality operator. This motivates us to extend association rules, from the simple equality operator, to a more general set of operators. We address the problem of mining general association rules in tabular data where rules can have all operators {?, >, ≠, =} in their antecedent part. The proposed algorithm, mining general rules (MGR), is applicable to datasets with discrete-ordered attributes and on quantitative discretized attributes. The proposed algorithm stores candidate general itemsets in a tree structure in such a way that supports of complex itemsets can be recursively computed from supports of simpler itemsets. The algorithm is shown to have benefits in terms of time complexity, memory management and has good potential for parallelization. 相似文献
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为了挖掘可疑通信的行为模式,定位发生了可疑通信行为的上网账户,本文首先分析了可疑通信行为特点。然后针对已有关联规则挖掘算法不能同时满足多层次数据挖掘和加权关联规则挖掘的问题,分析对比两种典型的基本关联规则算法,以FP-tree为基础,提出了ML-WFP多层次加权关联规则挖掘算法。针对算法中数据项权重的确定问题,由用户设置数据项间的重要性比较关系,借鉴模糊一致矩阵的概念,利用模糊层次分析法计算数据项的权重。最后将该算法应用于可疑通信行为的挖掘。实验测试结果表明可疑通信行为挖掘方案合理有效。 相似文献
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Cheng-Hsiung Weng 《Knowledge》2011,24(5):697-708
Association rule mining is an important data analysis method for the discovery of associations within data. There have been many studies focused on finding fuzzy association rules from transaction databases. Unfortunately, in the real world, one may have available relatively infrequent data, as well as frequent data. From infrequent data, we can find a set of rare itemsets that will be useful for teachers to find out which students need extra help in learning. While the previous association rules discovery techniques are able to discover some rules based on frequency, this is insufficient to determine the importance of a rule composed of frequency-based data items. To remedy this problem, we develop a new algorithm based on the Apriori approach to mine fuzzy specific rare itemsets from quantitative data. Finally, fuzzy association rules can be generated from these fuzzy specific rare itemsets. The patterns are useful to discover learning problems. Experimental results show that the proposed approach is able to discover interesting and valuable patterns from the survey data. 相似文献
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数据挖掘是从大量不完全的、有噪声的、模糊的、随机的数据中,提取隐含在其中的,事先不为人知的,但又潜在的有用的信息和知识的过程.使用一种挖掘算法,用簇集表格来存储数据库中的项目,利用最大值约束下的多最小支持度挖掘算法综合以得出关联规则. 相似文献
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针对单一层次结构实现规则提取具有规则提取准确性不高、算法运行时间长、难以满足用户使用需求的问题,提出一种基于改进多层次模糊关联规则的定量数据挖掘算法。采用高频项目集合,通过不断深化迭代的方法形成自顶向下的挖掘过程,整合模糊集合理论、数据挖掘算法以及多层次分类技术,从事务数据集中寻找模糊关联规则,挖掘出储存在多层次结构事务数据库中定量值信息的隐含知识,实现用户的定制化信息挖掘需求。实验结果表明,提出的数据挖掘算法在挖掘精度和运算时间方面相较于其他算法具有突出优势,可为多层次关联规则提取方法的实际应用带来新的发展空间。 相似文献
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文章基于FP-增长算法提出了一种新的挖掘复合项关联规则的算法。实验证明,该算法具有良好的可伸缩性和很高的运行效率,解决了复合项关联规则挖掘在实际应用中的效率瓶颈问题,适用于实际的大型数据库。 相似文献
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关联规则挖掘是数据挖掘的主要任务之一。为了进一步提高关联规则挖掘算法的认知特性和运算效果,提出了一种新的关联规则挖掘思想并由此构造了一种基于规则模糊认知图的关联规则挖掘算法。该算法使用规则模糊认知图进行知识表示,对每个挖掘到的关联规则进行可达模糊推理,从而减少了与数据库交互的次数。实验证明该方法与Apriori的关联规则算法相比,提高了关联规则挖掘的效率,增强了智能化程度。 相似文献
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An efficient approach to mining indirect associations 总被引:1,自引:0,他引:1
Discovering association rules is one of the important tasks in data mining. While most of the existing algorithms are developed
for efficient mining of frequent patterns, it has been noted recently that some of the infrequent patterns, such as indirect
associations, provide useful insight into the data. In this paper, we propose an efficient algorithm, called HI-mine, based on a new data structure, called HI-struct, for mining the complete set of indirect associations between items. Our experimental results show that HI-mine's performance is significantly better than that of the previously developed algorithm for mining indirect associations on
both synthetic and real world data sets over practical ranges of support specifications. 相似文献
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《Intelligent Data Analysis》1999,3(5):363-376
Data-mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values, however, transactions with quantitative values are commonly seen in real-world applications. This paper thus proposes a new data-mining algorithm for extracting interesting knowledge from transactions stored as quantitative values. The proposed algorithm integrates fuzzy set concepts and the apriori mining algorithm to find interesting fuzzy association rules in given transaction data sets. Experiments with student grades at I-Shou University were also made to verify the performance of the proposed algorithm. 相似文献
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传统的关联规则挖掘研究事务中所包含的项与项之间的关联性,而负关联规则挖掘不仅要考虑事务中包含的项,还要考虑事务中不包含的项。给出了完全负关联规则的定义,提出一种基于树的算法Free-PNP,通过此算法挖掘数据库中的负频繁模式,继而得到所要挖掘的完全负关联规则。通过实验验证了算法的有效性。 相似文献
15.
Mining fuzzy association rules from uncertain data 总被引:3,自引:3,他引:0
Association rule mining is an important data analysis method that can discover associations within data. There are numerous
previous studies that focus on finding fuzzy association rules from precise and certain data. Unfortunately, real-world data
tends to be uncertain due to human errors, instrument errors, recording errors, and so on. Therefore, a question arising immediately
is how we can mine fuzzy association rules from uncertain data. To this end, this paper proposes a representation scheme to
represent uncertain data. This representation is based on possibility distributions because the possibility theory establishes
a close connection between the concepts of similarity and uncertainty, providing an excellent framework for handling uncertain
data. Then, we develop an algorithm to mine fuzzy association rules from uncertain data represented by possibility distributions.
Experimental results from the survey data show that the proposed approach can discover interesting and valuable patterns with
high certainty. 相似文献
16.
Tzung-Pei Hong Kuei-Ying Lin Shyue-Liang Wang 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2006,10(10):925-932
Many researchers in database and machine learning fields are primarily interested in data mining because it offers opportunities to discover useful information and important relevant patterns in large databases. Most previous studies have shown how binary valued transaction data may be handled. Transaction data in real-world applications usually consist of quantitative values, so designing a sophisticated data-mining algorithm able to deal with various types of data presents a challenge to workers in this research field. In the past, we proposed a fuzzy data-mining algorithm to find association rules. Since sequential patterns are also very important for real-world applications, this paper thus focuses on finding fuzzy sequential patterns from quantitative data. A new mining algorithm is proposed, which integrates the fuzzy-set concepts and the AprioriAll algorithm. It first transforms quantitative values in transactions into linguistic terms, then filters them to find sequential patterns by modifying the AprioriAll mining algorithm. Each quantitative item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of the original items. The patterns mined out thus exhibit the sequential quantitative regularity in databases and can be used to provide some suggestions to appropriate supervisors. 相似文献
17.
Machine-learning and data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level. Transactions with quantitative values and items with hierarchical relationships are, however, commonly seen in real-world applications. This paper proposes a fuzzy multiple-level mining algorithm for extracting knowledge implicit in transactions stored as quantitative values. The proposed algorithm adopts a top-down progressively deepening approach to finding large itemsets. It integrates fuzzy-set concepts, data-mining technologies and multiple-level taxonomy to find fuzzy association rules from transaction data sets. Each item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of original items. The algorithm therefore focuses on the most important linguistic terms for reduced time complexity. 相似文献
18.
一种含负项目的一般化关联规则挖掘算法 总被引:3,自引:0,他引:3
传统的关联规则是形如A→B反映正项目之间关联关系的蕴涵式,它无法反映出数据之间隐藏的负关联关系.在表达式中引入负项目,将这种传统的关联规则扩展成包含正、负项目的一般化关联规则.介绍了一般化关联规则的概念及其相关性质定理,并加以证明,提出了一种基于频繁模式树的挖掘混合正、负项目的一般化关联规则的MGPNFP算法,对其性能进行了分析,并比较了MGPNFP算法比现有的挖掘含负项目关联规则的算法所具有的优势. 相似文献
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关联规则是数据挖掘研究中的一个重要的主题。一些算法都是假设数据中根本的关联基于时间是稳定的。然而,在现实世界领域,数据具有自己的特征,因此关联随着时间发生巨大的改变。现有的数据挖掘算法没有考虑关联的改变,这导致了严重的性能下降,特别是挖掘出的关联规则被用来分类和预测。尽管关联改变的挖掘是一个重要的问题,因为需要基于过去的历史数据来预测未来,现有的数据挖掘算法不符合这样的工作。文中引入模糊数据挖掘算法来发现基于时间的关联规则的改变。基于挖掘出的模糊规则,能预测关联规则在未来如何改变。实验表明了算法的有效性。 相似文献