Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful fo...Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for users.Thus,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid data.This approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.展开更多
Hierarchical clustering analysis based on statistic s is one of the most important mining algorithms, but the traditionary hierarchica l clustering method is based on global comparing, which only takes in Q clusteri n...Hierarchical clustering analysis based on statistic s is one of the most important mining algorithms, but the traditionary hierarchica l clustering method is based on global comparing, which only takes in Q clusteri ng while ignoring R clustering in practice, so it has some limitation especially when the number of sample and index is very large. Furthermore, because of igno ring the association between the different indexes, the clustering result is not good & true. In this paper, we present the model and the algorithm of two-level hierarchi cal clustering which integrates Q clustering with R clustering. Moreover, becaus e two-level hierarchical clustering is based on the respective clustering resul t of each class, the classification of the indexes directly effects on the a ccuracy of the final clustering result, how to appropriately classify the inde xes is the chief and difficult problem we must handle in advance. Although some literatures also have referred to the issue of the classificati on of the indexes, but the articles classify the indexes only according to their superficial signification, which is unscientific. The reasons are as follow s: First, the superficial signification of some indexes usually takes on different meanings and it is easy to be misapprehended by different person. Furthermore, t his classification method seldom make use of history data, the classification re sult is not so objective. Second, for some indexes, its superficial signification didn’t show any mean ings, so simply from the superficial signification, we can’t classify them to c ertain classes. Third, this classification method need the users have higher level knowledge of this field, otherwise it is difficult for the users to understand the signifi cation of some indexes, which sometimes is not available. So in this paper, to this question, we first use R clustering method to cluste ring indexes, dividing p dimension indexes into q classes, then adopt two-level clustering method to get the final result. Obviously, the classification result is more objective and accurate. Moreover, after the first step, we can get the relation of the different indexes and their interaction. We can also know under a certain class indexes, which samples can be clustering to a class. (These semi finished results sometimes are very useful.) The experiments also indicates the effective and accurate of the algorithms. And, the result of R clustering ca n be easily used for the later practice.展开更多
针对利用海量数据构建分类模型时训练数据规模大、训练时间长且碳排放量大的问题,提出面向低能耗高性能的分类器两阶段数据选择方法TSDS(Two-Stage Data Selection)。首先,通过修正余弦相似度确定聚类中心,并将样本数据进行基于不相似...针对利用海量数据构建分类模型时训练数据规模大、训练时间长且碳排放量大的问题,提出面向低能耗高性能的分类器两阶段数据选择方法TSDS(Two-Stage Data Selection)。首先,通过修正余弦相似度确定聚类中心,并将样本数据进行基于不相似点的分裂层次聚类;其次,对聚类结果按数据分布自适应抽样以组成高质量的子样本集;最后,利用子样本集在分类模型上训练,在加速训练过程的同时提升模型精度。在Spambase、Bupa和Phoneme等6个数据集上构建支持向量机(SVM)和多层感知机(MLP)分类模型,验证TSDS的性能。实验结果表明在样本数据压缩比达到85.00%的情况下,TSDS能将分类模型准确率提升3~10个百分点,同时加速模型训练,使训练SVM分类器的能耗平均降低93.76%,训练MLP分类器的能耗平均降低75.41%。可见,TSDS在大数据场景的分类任务上既能缩短训练时间和减少能耗,又能提升分类器性能,从而助力实现“双碳”目标。展开更多
In this paper, an estimation method for reliability parameter in the case of zero-failuare data-synthetic estimation method is given. For zero-failure data of double-parameter exponential distribution, a hierarchical ...In this paper, an estimation method for reliability parameter in the case of zero-failuare data-synthetic estimation method is given. For zero-failure data of double-parameter exponential distribution, a hierarchical Bayesian estimation of the failure probability is presented. After failure information is introduced, hierarchical Bayesian estimation and synthetic estimation of the failure probability, as well as synthetic estimation of reliability are given. Calculation and analysis are performed regarding practical problems in case that life distribution of an engine obeys double-parameter exponential distribution.展开更多
基金The research was supported by the National Natural Science Foundation of China under grant No:60775036, 60970061the Higher Education Nature Science Research Fund Project of Jiangsu Province under grant No: 09KJD520004.
文摘Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for users.Thus,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid data.This approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.
文摘Hierarchical clustering analysis based on statistic s is one of the most important mining algorithms, but the traditionary hierarchica l clustering method is based on global comparing, which only takes in Q clusteri ng while ignoring R clustering in practice, so it has some limitation especially when the number of sample and index is very large. Furthermore, because of igno ring the association between the different indexes, the clustering result is not good & true. In this paper, we present the model and the algorithm of two-level hierarchi cal clustering which integrates Q clustering with R clustering. Moreover, becaus e two-level hierarchical clustering is based on the respective clustering resul t of each class, the classification of the indexes directly effects on the a ccuracy of the final clustering result, how to appropriately classify the inde xes is the chief and difficult problem we must handle in advance. Although some literatures also have referred to the issue of the classificati on of the indexes, but the articles classify the indexes only according to their superficial signification, which is unscientific. The reasons are as follow s: First, the superficial signification of some indexes usually takes on different meanings and it is easy to be misapprehended by different person. Furthermore, t his classification method seldom make use of history data, the classification re sult is not so objective. Second, for some indexes, its superficial signification didn’t show any mean ings, so simply from the superficial signification, we can’t classify them to c ertain classes. Third, this classification method need the users have higher level knowledge of this field, otherwise it is difficult for the users to understand the signifi cation of some indexes, which sometimes is not available. So in this paper, to this question, we first use R clustering method to cluste ring indexes, dividing p dimension indexes into q classes, then adopt two-level clustering method to get the final result. Obviously, the classification result is more objective and accurate. Moreover, after the first step, we can get the relation of the different indexes and their interaction. We can also know under a certain class indexes, which samples can be clustering to a class. (These semi finished results sometimes are very useful.) The experiments also indicates the effective and accurate of the algorithms. And, the result of R clustering ca n be easily used for the later practice.
文摘针对利用海量数据构建分类模型时训练数据规模大、训练时间长且碳排放量大的问题,提出面向低能耗高性能的分类器两阶段数据选择方法TSDS(Two-Stage Data Selection)。首先,通过修正余弦相似度确定聚类中心,并将样本数据进行基于不相似点的分裂层次聚类;其次,对聚类结果按数据分布自适应抽样以组成高质量的子样本集;最后,利用子样本集在分类模型上训练,在加速训练过程的同时提升模型精度。在Spambase、Bupa和Phoneme等6个数据集上构建支持向量机(SVM)和多层感知机(MLP)分类模型,验证TSDS的性能。实验结果表明在样本数据压缩比达到85.00%的情况下,TSDS能将分类模型准确率提升3~10个百分点,同时加速模型训练,使训练SVM分类器的能耗平均降低93.76%,训练MLP分类器的能耗平均降低75.41%。可见,TSDS在大数据场景的分类任务上既能缩短训练时间和减少能耗,又能提升分类器性能,从而助力实现“双碳”目标。
文摘In this paper, an estimation method for reliability parameter in the case of zero-failuare data-synthetic estimation method is given. For zero-failure data of double-parameter exponential distribution, a hierarchical Bayesian estimation of the failure probability is presented. After failure information is introduced, hierarchical Bayesian estimation and synthetic estimation of the failure probability, as well as synthetic estimation of reliability are given. Calculation and analysis are performed regarding practical problems in case that life distribution of an engine obeys double-parameter exponential distribution.
