For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FC...For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FCM and particle swarm optimization(PSO)clustering algorithm,and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization(AF-APSO).The experiment shows that the AF-APSO can avoid local optima,and get the best fitness and clustering performance significantly.展开更多
Software technology based on reuse is identified as a process of designing software for the reuse purpose. The software reuse is a process in which the existing software is used to build new software. A metric is a qu...Software technology based on reuse is identified as a process of designing software for the reuse purpose. The software reuse is a process in which the existing software is used to build new software. A metric is a quantitative indicator of an attribute of an item/thing. Reusability is the likelihood for a segment of source code that can be used again to add new functionalities with slight or no modification. A lot of research has been projected using reusability in reducing code, domain, requirements, design etc., but very little work is reported using software reuse in medical domain. An attempt is made to bridge the gap in this direction, using the concepts of clustering and classifying the data based on the distance measures. In this paper cardiologic database is considered for study. The developed model will be useful for Doctors or Para-medics to find out the patient’s level in the cardiologic disease, deduce the medicines required in seconds and propose them to the patient. In order to measure the reusability K-means clustering algorithm is used.展开更多
A new no-reference blocking artifact metric for B-DCT compression video is presented in this paper. We first present a new definition of blocking artifact and a new method for measuring perceptive blocking artifact ba...A new no-reference blocking artifact metric for B-DCT compression video is presented in this paper. We first present a new definition of blocking artifact and a new method for measuring perceptive blocking artifact based on HVS taking into account the luminance masking and activity masking characteristic. Then, we propose a new concept of blocking artifact cluster and the algorithm for clustering blocking artifacts. Considering eye movement and fixation, we select several clusters with most serious blocking artifacts and utilize the average of their blocking artifacts to assess the total blocking artifact of B-DCT reconstructed video. Experimental results illustrating the performance of the proposed method are presented and evaluated.展开更多
Class cohesion is considered as one of the most important object-oriented software attributes. High cohesion is, in fact, a desirable property of software. Many different metrics have been suggested in the last severa...Class cohesion is considered as one of the most important object-oriented software attributes. High cohesion is, in fact, a desirable property of software. Many different metrics have been suggested in the last several years to measure the cohesion of classes in object-oriented systems. The class of structural object-oriented cohesion metrics is the most in-vestigated category of cohesion metrics. These metrics measure cohesion on structural information extracted from the source code. Empirical studies noted that these metrics fail in many situations to properly reflect cohesion of classes. This paper aims at exploring the use of hierarchical clustering techniques to improve the measurement of cohesion of classes in object-oriented systems. The proposed approach has been evaluated using three particular case studies. We also used in our study three well-known structural cohesion metrics. The achieved results show that the new approach appears to better reflect the cohesion (and structure) of classes than traditional structural cohesion metrics.展开更多
In order to quantitatively analyze air traffic operation complexity,multidimensional metrics were selected based on the operational characteristics of traffic flow.The kernel principal component analysis method was ut...In order to quantitatively analyze air traffic operation complexity,multidimensional metrics were selected based on the operational characteristics of traffic flow.The kernel principal component analysis method was utilized to reduce the dimensionality of metrics,therefore to extract crucial information in the metrics.The hierarchical clustering method was used to analyze the complexity of different airspace.Fourteen sectors of Guangzhou Area Control Center were taken as samples.The operation complexity of traffic situation in each sector was calculated based on real flight radar data.Clustering analysis verified the feasibility and rationality of the method,and provided a reference for airspace operation and management.展开更多
This article is an addendum to the 2001 paper [1] which investigated an approach to hierarchical clustering based on the level sets of a density function induced on data points in a d-dimensional feature space. We ref...This article is an addendum to the 2001 paper [1] which investigated an approach to hierarchical clustering based on the level sets of a density function induced on data points in a d-dimensional feature space. We refer to this as the “level-sets approach” to hierarchical clustering. The density functions considered in [1] were those formed as the sum of identical radial basis functions centered at the data points, each radial basis function assumed to be continuous, monotone decreasing, convex on every ray, and rising to positive infinity at its center point. Such a framework can be investigated with respect to both the Euclidean (L2) and Manhattan (L1) metrics. The addendum here puts forth some observations and questions about the level-sets approach that go beyond those in [1]. In particular, we detail and ask the following questions. How does the level-sets approach compare with other related approaches? How is the resulting hierarchical clustering affected by the choice of radial basis function? What are the structural properties of a function formed as the sum of radial basis functions? Can the levels-sets approach be theoretically validated? Is there an efficient algorithm to implement the level-sets approach?展开更多
针对区域配网变压器(简称“配变”)数量多,大量新型负荷、分布式光伏等接入,配变电压随机性波动增强,台区用户电压质量面临挑战。为更好地对区域配变电压进行越限特征分析及预测,提出了基于关联特征筛选的双层聚类区域配变电压预测方法...针对区域配网变压器(简称“配变”)数量多,大量新型负荷、分布式光伏等接入,配变电压随机性波动增强,台区用户电压质量面临挑战。为更好地对区域配变电压进行越限特征分析及预测,提出了基于关联特征筛选的双层聚类区域配变电压预测方法。首先,将区域配变的越限天数作为第一层聚类特征,获得电压性质正常以及越上限的配变。其次,针对电压越限配变提出结合Pearson相关系数和欧氏距离(Euclidean distance)的最优度量矩阵,提取原有数据的内含信息,作为K均值聚类(K-means)的输入,实现对区域配变双层聚类。在此基础上,选取该集群中代表配变表征该类配变,利用卷积双向长短期记忆网络-注意力机制(convolutional neural network-bidirectional long and short-term memory-attention,CNN-BiLSTM-Attention)模型对配变电压进行预测,该模型能够提取输入数据的双向信息特征,并对重要特征加权,从多时间尺度上获得双向特征信息用于预测。最后,在上海市某区域配变验证了该方法的有效性。展开更多
提出了一种基于DI-FCM(double indices fuzzy C-means)算法框架的无监督距离学习算法——基于混合距离学习的双指数模糊C均值算法HDDI-FCM(double indices fuzzy C-m eans with hybrid distance).数据集未知距离度量被表示为若干已有距...提出了一种基于DI-FCM(double indices fuzzy C-means)算法框架的无监督距离学习算法——基于混合距离学习的双指数模糊C均值算法HDDI-FCM(double indices fuzzy C-m eans with hybrid distance).数据集未知距离度量被表示为若干已有距离的线性组合,然后执行HDDI-FCM,在对数据集进行有效聚类的同时进行距离学习.为了保证迭代算法收敛,引入了Steffensen迭代法来改进计算簇中心点的迭代公式.讨论了算法中参数的选择.基于UCI(University of California,Irvine)数据集的实验结果表明该算法是有效的.展开更多
基金the China Agriculture Research System(No.CARS-49)Jiangsu College of Humanities and Social Sciences Outside Campus Research Base & Chinese Development of Strategic Research Base for Internet of Things
文摘For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FCM and particle swarm optimization(PSO)clustering algorithm,and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization(AF-APSO).The experiment shows that the AF-APSO can avoid local optima,and get the best fitness and clustering performance significantly.
文摘Software technology based on reuse is identified as a process of designing software for the reuse purpose. The software reuse is a process in which the existing software is used to build new software. A metric is a quantitative indicator of an attribute of an item/thing. Reusability is the likelihood for a segment of source code that can be used again to add new functionalities with slight or no modification. A lot of research has been projected using reusability in reducing code, domain, requirements, design etc., but very little work is reported using software reuse in medical domain. An attempt is made to bridge the gap in this direction, using the concepts of clustering and classifying the data based on the distance measures. In this paper cardiologic database is considered for study. The developed model will be useful for Doctors or Para-medics to find out the patient’s level in the cardiologic disease, deduce the medicines required in seconds and propose them to the patient. In order to measure the reusability K-means clustering algorithm is used.
基金Project (No. YJCB2003017MU) supported by Huawei Technology Fund, China
文摘A new no-reference blocking artifact metric for B-DCT compression video is presented in this paper. We first present a new definition of blocking artifact and a new method for measuring perceptive blocking artifact based on HVS taking into account the luminance masking and activity masking characteristic. Then, we propose a new concept of blocking artifact cluster and the algorithm for clustering blocking artifacts. Considering eye movement and fixation, we select several clusters with most serious blocking artifacts and utilize the average of their blocking artifacts to assess the total blocking artifact of B-DCT reconstructed video. Experimental results illustrating the performance of the proposed method are presented and evaluated.
文摘Class cohesion is considered as one of the most important object-oriented software attributes. High cohesion is, in fact, a desirable property of software. Many different metrics have been suggested in the last several years to measure the cohesion of classes in object-oriented systems. The class of structural object-oriented cohesion metrics is the most in-vestigated category of cohesion metrics. These metrics measure cohesion on structural information extracted from the source code. Empirical studies noted that these metrics fail in many situations to properly reflect cohesion of classes. This paper aims at exploring the use of hierarchical clustering techniques to improve the measurement of cohesion of classes in object-oriented systems. The proposed approach has been evaluated using three particular case studies. We also used in our study three well-known structural cohesion metrics. The achieved results show that the new approach appears to better reflect the cohesion (and structure) of classes than traditional structural cohesion metrics.
基金co-supported by the National Natural Science Foundation of China(No.61304190)the Fundamental Research Funds for the Central Universities of China(No.NJ20150030)the Youth Science and Technology Innovation Fund(No.NS2014067)
文摘In order to quantitatively analyze air traffic operation complexity,multidimensional metrics were selected based on the operational characteristics of traffic flow.The kernel principal component analysis method was utilized to reduce the dimensionality of metrics,therefore to extract crucial information in the metrics.The hierarchical clustering method was used to analyze the complexity of different airspace.Fourteen sectors of Guangzhou Area Control Center were taken as samples.The operation complexity of traffic situation in each sector was calculated based on real flight radar data.Clustering analysis verified the feasibility and rationality of the method,and provided a reference for airspace operation and management.
文摘This article is an addendum to the 2001 paper [1] which investigated an approach to hierarchical clustering based on the level sets of a density function induced on data points in a d-dimensional feature space. We refer to this as the “level-sets approach” to hierarchical clustering. The density functions considered in [1] were those formed as the sum of identical radial basis functions centered at the data points, each radial basis function assumed to be continuous, monotone decreasing, convex on every ray, and rising to positive infinity at its center point. Such a framework can be investigated with respect to both the Euclidean (L2) and Manhattan (L1) metrics. The addendum here puts forth some observations and questions about the level-sets approach that go beyond those in [1]. In particular, we detail and ask the following questions. How does the level-sets approach compare with other related approaches? How is the resulting hierarchical clustering affected by the choice of radial basis function? What are the structural properties of a function formed as the sum of radial basis functions? Can the levels-sets approach be theoretically validated? Is there an efficient algorithm to implement the level-sets approach?
文摘针对区域配网变压器(简称“配变”)数量多,大量新型负荷、分布式光伏等接入,配变电压随机性波动增强,台区用户电压质量面临挑战。为更好地对区域配变电压进行越限特征分析及预测,提出了基于关联特征筛选的双层聚类区域配变电压预测方法。首先,将区域配变的越限天数作为第一层聚类特征,获得电压性质正常以及越上限的配变。其次,针对电压越限配变提出结合Pearson相关系数和欧氏距离(Euclidean distance)的最优度量矩阵,提取原有数据的内含信息,作为K均值聚类(K-means)的输入,实现对区域配变双层聚类。在此基础上,选取该集群中代表配变表征该类配变,利用卷积双向长短期记忆网络-注意力机制(convolutional neural network-bidirectional long and short-term memory-attention,CNN-BiLSTM-Attention)模型对配变电压进行预测,该模型能够提取输入数据的双向信息特征,并对重要特征加权,从多时间尺度上获得双向特征信息用于预测。最后,在上海市某区域配变验证了该方法的有效性。
文摘提出了一种基于DI-FCM(double indices fuzzy C-means)算法框架的无监督距离学习算法——基于混合距离学习的双指数模糊C均值算法HDDI-FCM(double indices fuzzy C-m eans with hybrid distance).数据集未知距离度量被表示为若干已有距离的线性组合,然后执行HDDI-FCM,在对数据集进行有效聚类的同时进行距离学习.为了保证迭代算法收敛,引入了Steffensen迭代法来改进计算簇中心点的迭代公式.讨论了算法中参数的选择.基于UCI(University of California,Irvine)数据集的实验结果表明该算法是有效的.
