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Rong Zhang Kaoru Shimada Shingo Mabu Kotaro Hirasawa 《IEEJ Transactions on Electrical and Electronic Engineering》2014,9(4):398-406
Attribute selection is a technique to prune less relevant information and discover high‐quality knowledge. It is especially useful for the classification of a large database, because the preprocessing of data increases the possibility that predictor attributes given to the mining algorithm become more relevant to the class attribute. In this paper, a method to acquire the optimal attribute subset for the genetic network programming (GNP) based class association rule mining has been proposed, and this attribute selection process using genetic algorithm (GA) leads to a higher accuracy for classification. Class association rule mining through GNP is conducted with a small subset of data rather than the original large number of attributes; thus simple but important rules are obtained for classification while the local optimal problem is avoided. Simulation results with educational data show that the classification accuracy is largely improved from 52.73 to 74.54%, when classification is made using the optimal attribute subset. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. 相似文献
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Nannan Lu Shingo Mabu Tuo Wang Kotaro Hirasawa 《IEEJ Transactions on Electrical and Electronic Engineering》2013,8(2):164-172
Genetic network programming (GNP)‐based class association rule mining has been demonstrated to be efficient for misuse and anomaly detection. However, misuse detection is weak in detecting brand new attacks, while anomaly detection has a defect of high positive false rate. In this paper, a unified detection method is proposed to integrate misuse detection and anomaly detection to overcome their disadvantages. In addition, GNP‐based class association rule mining method extracts an overwhelming number of rules which contain much redundant and irrelevant information. Therefore, in this paper, an efficient class association rule‐pruning method is proposed based on matching degree and genetic algorithm (GA). In the first stage, a matching degree‐based method is applied to preprune the rules in order to improve the efficiency of the GA. In the second stage, the GA is implemented to pick up the effective rules among the rules remaining in the first stage. Simulations on KDDCup99 show the high performance of the proposed method. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. 相似文献
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Yuchen Yang Shingo Mabu Kaoru Shimada Kotaro Hirasawa 《IEEJ Transactions on Electrical and Electronic Engineering》2011,6(4):353-360
Intertransaction class association rule (interCAR) has the ability to find the relationships among attributes from different transactions, which has shown its effectiveness for stock market prediction. A crisp interCAR mining method based on Genetic Network Programming (GNP) has been studied in our previous work. But, the crisp method loses much useful information in the discretization and it has many unstable factors influencing the prediction results, so more information is desired in order to make the prediction safer and more efficient. In this paper, a fuzzy interCAR mining method is proposed to keep as much information as possible in the data transformation. Besides, the proposed method has ability that the trading actions bring large profits. The proposed method is applied to Tokyo Stock Exchange, where we compared it with the crisp method as well as some other methods. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. 相似文献
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Eloy Gonzales Shingo Mabu Karla Taboada Kaoru Shimada Kotaro Hirasawa 《IEEJ Transactions on Electrical and Electronic Engineering》2011,6(5):431-440
During the last years, several association rule‐based classification methods have been proposed, these algorithms may quickly generate accurate rules. However, the generated rules are often very large in terms of the number of rules and usually complex and hardly understandable for users. Among all the rules generated by the algorithms, only some of them are likely to be of any interest to the domain expert analyzing the data. Most of the rules are either redundant, irrelevant or obvious. In this paper, a new method for selecting the interesting class association rules is proposed by an evolutionary method named genetic relation algorithm. The algorithm evaluates the relevance and interestingness of the discovered association rules by the relationships between the rules in each generation using a specific measure of distance among them giving a reduced set of rules as the result in the final generation. This small rule set has the following properties: (i) accurate as it has at least the same classification accuracy as the complete association rule set, (ii) interesting because of the diversity of rules and (iii) comprehensible because it is more understandable for the users as the number of attributes involved in the rules is also small. The efficiency of the proposed method is compared with other conventional methods including genetic network programming‐based mining using ten databases and the experimental results show that it outperforms others keeping a good balance between the classification accuracy and the comprehensibility of the rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. 相似文献
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介绍了数据挖掘技术中的关联规则挖掘算法的基本概念,以及运用该算法建立汽轮机运行模型的方法,以某汽轮机轴系振动的数据挖掘为算例,详细说明了通过关联规则挖掘算法,从大量汽轮机振动监测数据中确定轴系振动范围的过程。最后指出,支持度、置信度阈值的设置对挖掘结果有很大的影响。 相似文献
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对凝汽器传热端差的各个主要影响因素及它们之间的关系进行了分析.将关联规则挖掘技术应用于凝汽器运行数据的分析,获得了有益的分析结果.对挖掘结果的分析表明,该技术可用于凝汽器的性能分析、状态监测、故障诊断和状态检修等方面,很有意义. 相似文献
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基于Apriori算法的二次设备缺陷数据挖掘与分析方法 总被引：3，自引：0，他引：3
为提升电力系统二次设备的运维和管控水平,从二次设备的缺陷数据出发,提出了基于Apriori算法的二次设备缺陷数据挖掘与分析方法。首先,分析了关联规则与Apriori算法的基本思路,然后建立了基于关联规则的二次设备缺陷模型,在模型中考虑了二次设备缺陷的几个重要属性:二次设备的生产厂家、设备类型、设备缺陷的原因、发生缺陷的设备部位以及缺陷等级。进一步,以一组自动化设备缺陷数据为例,阐述了基于Apriori算法的二次设备缺陷数据挖掘和分析方法,分析结果表明所提方法能够用于寻找二次设备的薄弱环节,并能够找到诱发薄弱环节的原因,同时还具有分析设备家族性缺陷等功能。 相似文献
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Ci Chen Shingo Mabu Kaoru Shimada Kotaro Hirasawa 《IEEJ Transactions on Electrical and Electronic Engineering》2010,5(5):553-559
Because of the expansion of the Internet in recent years, computer systems are exposed to an increasing number and type of security threats. How to detect network intrusions effectively becomes an important technique. This paper proposes a class association rule mining approach based on genetic network programming (GNP) for detecting network intrusions. This approach can deal with both discrete and continuous attributes in network‐related data. And it can be flexibly applied to both misuse detection and anomaly detection. Experimental results with KDD99Cup and DARPA98 database from MIT Lincoln Laboratory shows that the proposed method provides a competitive high detection rate (DR) compared to other machine learning techniques. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. 相似文献
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电网运维数据表明,电网设备故障主要由雷电、雨雪、大风、冰冻等自然气象因素引起,是电网防灾减灾的重点。基于气象与电网故障之间的关联特点,提出一种基于设备脆弱性的电网设备气象灾害故障预测方法。将设备脆弱性指数作为变量改进关联规则算法,实现设备不同历史状态下的归一化处理。算例分析表明,基于设备脆弱性改进的关联方法,能够准确、全面地建立气象信息与电网故障之间的映射关系,预测给定气象条件下的故障概率。 相似文献
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提出基于多维时间序列关联分析的电力设备故障预测方法:将电力设备拓扑网络设备节点的历史时间序列数据进行规范化,运用时间序列分解算法将时间序列进行分解;用一种时间序列模式表示方法,提取关键设备发生故障之前网络拓扑中设备节点的特征事件;并采用关联分析的方法挖掘设备指标运行趋势与设备工况之间存在的隐含关系,达到对故障或冲击进行有效预测的目的。实验证明,该方法能充分利用时间序列数据,发挥数据挖掘对于不确定性关系的分析和表达的优势,能够准确、有效地进行复杂电力设备故障预测。 相似文献
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在传统的Apriori关联规则挖掘算法分析基础上,针对目前多最小支持度和增量式关联规则挖掘的局限性,提出基于多最小支持度的增量式关联规则挖掘算法。该算法适用于事务出现频率一致及不一致的情况,利用多最小支持度能挖掘出更有意义的结果;同时,该算法还能实现事务数据不断增加时的数据挖掘,提高了挖掘的效率。应用电力客户信用数据库进行实验的结果表明,改进算法能有效挖掘出稀有项,分析出潜在的信用风险客户,对电力客户信用评价具有辅助决策作用。 相似文献
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中国全社会用电量增长主导因素辨识 总被引：5，自引：0，他引：5
目前负荷中长期预测时选取相关的社会经济指标没有统一的理论依据.文中将数据挖掘技术应用到电量增长的关联性分析中,从全国30个省(自治区、直辖市)的历史数据中选取25项相关指标,采用3种不同的隶属度函数进行赋值.在此基础上利用Apriori算法计算不同指标与用电量增长相关的模糊置信度,辨识出国内生产总值(GDP)、工业总产值、进出口总额、固定资产投资、居民人均可支配收入等与用电量增长较为相关的主导因素,并结合自组织映射神经网络获取中国用电量增长的一般性规律.该研究思路为年度负荷预测相关因素的选取提供新的策略. 相似文献
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Shingo Mabu Takuro Higuchi Takashi Kuremoto 《IEEJ Transactions on Electrical and Electronic Engineering》2020,15(5):733-740
Data mining extracts useful knowledge from big data. The extracted knowledge in data mining is often represented by association rules, and association rules can be also used for classification. However, when association rules for classification (called class association rules) are extracted, a large number of data with class labels are necessary, which requires a lot of cost of manual annotation. Therefore, this paper proposes a semisupervised learning method for rule extraction, where a small number of labeled data and a large number of unlabeled data are used to efficiently extract class association rules. In detail, this paper proposes two types of classifiers using class association rules: one is a classifier based on semisupervised learning, and the other is that based on both supervised and semisupervised learning. The second method builds several classifiers using supervised learning and semisupervised learning and the classification results of these classifiers are integrated to make the final decision. As an association rule mining method, Genetic Network Programming (GNP), which is one of the graph‐based evolutionary algorithms is used. GNP has shown distinguished classification ability in some applications; therefore, one of the other objectives of this paper is to extend the applicability of GNP to data mining based on semisupervised learning. In the experiments, the classification accuracy is evaluated using some benchmark datasets by comparing with some conventional methods. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. 相似文献
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电能表的质量问题直接影响了用户侧的供电可靠性与安全性,关系到国家电网系统稳定与经济运行。针对目前电能表质量存在的不足,应用Apriori关联规则数据挖掘方法,将7种检定试验结果作为质量评估指标,对这7类影响因素做了关联分析,得到了各个检定试验项目对检定质量的影响程度。同时探讨了检定项目的内部关联,找出了高频连带出现的不合格检定项目。在分析这些检定项高频出现的原因的基础上,得出了电能表质量薄弱点集中在计量模块这一结论。为提高检定通过率、提高产品质量提供了参考。 相似文献