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运用模糊集挖掘数量属性数据的关联规则 总被引:3,自引:0,他引:3
绝大多数关联规则的挖掘方法基于布尔属性数据,但在现实应用中会经常需要对数量属性的数据进行关联挖掘。该文就提出一种算法,在经典Apriori后选集算法的基础上引入了模糊逻辑集合的概念,将数据集中的数量属性按照模糊集合定义进行划分从而将原始事务数据转化成基于模糊集的数据,然后再运用Apriori算法发现潜在的关联规则。 相似文献
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基于日历约束的时序关联规则挖掘由于其实用性,越来越受到研究者的关注。由于现实中用户很难对时间模式进行精确描述,因此基于模糊日历的时序关联规则挖掘更有现实意义。借助模糊概念和模糊运算,对时间区间的描述很容易实现。对于用户指定的日历模式,不同的时间区间可根据它们的隶属度具有不同的权重。在模糊日历代数的基础上,结合增量挖掘和累进计数的思想,本文提出了一种基于模糊日历约束的关联规则挖掘方法,理论分析和实验结果均表明,该算法是高效可行的。 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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提出了一种新的基于模糊概念的量化关联规则挖掘方法,该方法利用在量化属性域上定义的一组模糊概念表示属性间的关联关系,克服了传统的离散分区法的不足,使得规则的表示自然,简明,有利于专家理解,同时,给出了挖掘算法。 相似文献
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An ACS-based framework for fuzzy data mining 总被引:1,自引:0,他引:1
Tzung-Pei Hong Ya-Fang Tung Shyue-Liang Wang Min-Thai Wu Yu-Lung Wu 《Expert systems with applications》2009,36(9):11844-11852
Data mining is often used to find out interesting and meaningful patterns from huge databases. It may generate different kinds of knowledge such as classification rules, clusters, association rules, and among others. A lot of researches have been proposed about data mining and most of them focused on mining from binary-valued data. Fuzzy data mining was thus proposed to discover fuzzy knowledge from linguistic or quantitative data. Recently, ant colony systems (ACS) have been successfully applied to optimization problems. However, few works have been done on applying ACS to fuzzy data mining. This thesis thus attempts to propose an ACS-based framework for fuzzy data mining. In the framework, the membership functions are first encoded into binary-bits and then fed into the ACS to search for the optimal set of membership functions. The problem is then transformed into a multi-stage graph, with each route representing a possible set of membership functions. When the termination condition is reached, the best membership function set (with the highest fitness value) can then be used to mine fuzzy association rules from a database. At last, experiments are made to make a comparison with other approaches and show the performance of the proposed framework. 相似文献
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《Expert systems with applications》2014,41(2):655-662
Data mining is most commonly used in attempts to induce association rules from transaction data. In the past, we used the fuzzy and GA concepts to discover both useful fuzzy association rules and suitable membership functions from quantitative values. The evaluation for fitness values was, however, quite time-consuming. Due to dramatic increases in available computing power and concomitant decreases in computing costs over the last decade, learning or mining by applying parallel processing techniques has become a feasible way to overcome the slow-learning problem. In this paper, we thus propose a parallel genetic-fuzzy mining algorithm based on the master–slave architecture to extract both association rules and membership functions from quantitative transactions. The master processor uses a single population as a simple genetic algorithm does, and distributes the tasks of fitness evaluation to slave processors. The evolutionary processes, such as crossover, mutation and production are performed by the master processor. It is very natural and efficient to run the proposed algorithm on the master–slave architecture. The time complexities for both sequential and parallel genetic-fuzzy mining algorithms have also been analyzed, with results showing the good effect of the proposed one. When the number of generations is large, the speed-up can be nearly linear. The experimental results also show this point. Applying the master–slave parallel architecture to speed up the genetic-fuzzy data mining algorithm is thus a feasible way to overcome the low-speed fitness evaluation problem of the original algorithm. 相似文献
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Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In the past, many approaches based on regression, neural networks and other mathematical models were proposed to analyze the time series. In this paper, we attempt to use the data mining technique to analyze time series. Many previous studies on data mining have focused on handling binary-valued data. Time series data, however, are usually quantitative values. We thus extend our previous fuzzy mining approach for handling time-series data to find linguistic association rules. The proposed approach first uses a sliding window to generate continues subsequences from a given time series and then analyzes the fuzzy itemsets from these subsequences. Appropriate post-processing is then performed to remove redundant patterns. Experiments are also made to show the performance of the proposed mining algorithm. Since the final results are represented by linguistic rules, they will be friendlier to human than quantitative representation. 相似文献
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关联规则挖掘AprioriTid算法优化研究 总被引:19,自引:0,他引:19
提出了一种基于事务压缩和项目压缩的AprioriTid优化算法。该算法的特点是:项目集采用关键字识别,同时对事务数据进行事务和项目压缩。从而省去了Apriori算法和AprioriTid算法中的剪枝和模式匹配步骤,减小了扫描事务数据库的大小,提高了发现规则的效率。通过实验表明,优化的算法执行效率明显优于AprioriTid算法。 相似文献
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A Novel Approach for Combining Fuzzy Rules Using Mean Operators for Effective Rule Reduction 总被引:1,自引:1,他引:0
Aarthi Chandramohan M. V. C. Rao 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2006,10(11):1103-1108
This paper will present a novel method based on harmonic mean, geometric mean, arithmetic mean and root mean square to help reduce fuzzy rules. The objective of the new method proposed is to produce fuzzy models with both a small number of interpretable rules and sufficiently high precision. Comparisons will be made between systems utilizing reduced rules and original rules to verify efficacy of the new methods in terms of the defuzzified outputs. As a practical example of a nonlinear system, an inverted pendulum will be controlled by a minimal set of rules to illustrate the performance and applicability of the proposed method. 相似文献