PEU à PEU属于江南布衣旗下的运动品牌,本项目位于杭州,店铺面积70平方米,空间及装置设计皆由Sò Studio完成。空间设计最初的灵感来自于运动场上的相互作用力:捆绑挤压和拉伸释放。用弹力的可塑性、器械的秩序感和移动既视的...PEU à PEU属于江南布衣旗下的运动品牌,本项目位于杭州,店铺面积70平方米,空间及装置设计皆由Sò Studio完成。空间设计最初的灵感来自于运动场上的相互作用力:捆绑挤压和拉伸释放。用弹力的可塑性、器械的秩序感和移动既视的装置来呈现出空间的模样。有秩序感却又灵活可变的货架本身成为了空间的分割,货架上方的轨道带动着在设定路线下匀速运动的球体装置。展开更多
This paper is concerned about studying modeling-based methods in cluster analysis to classify data elements into clusters and thus dealing with time series in view of this classification to choose the appropriate mixe...This paper is concerned about studying modeling-based methods in cluster analysis to classify data elements into clusters and thus dealing with time series in view of this classification to choose the appropriate mixed model. The mixture-model cluster analysis technique under different covariance structures of the component densities is presented. This model is used to capture the compactness, orientation, shape, and the volume of component clusters in one expert system to handle Gaussian high dimensional heterogeneous data set. To achieve flexibility in currently practiced cluster analysis techniques. The Expectation-Maximization (EM) algorithm is considered to estimate the parameter of the covariance matrix. To judge the goodness of the models, some criteria are used. These criteria are for the covariance matrix produced by the simulation. These models have not been tackled in previous studies. The results showed the superiority criterion ICOMP PEU to other criteria.<span> </span><span>This is in addition to the success of the model based on Gaussian clusters in the prediction by using covariance matrices used in this study. The study also found the possibility of determining the optimal number of clusters by choosing the number of clusters corresponding to lower values </span><span><span><span>for the different criteria used in the study</span></span></span><span><span><span>.展开更多
文摘PEU à PEU属于江南布衣旗下的运动品牌,本项目位于杭州,店铺面积70平方米,空间及装置设计皆由Sò Studio完成。空间设计最初的灵感来自于运动场上的相互作用力:捆绑挤压和拉伸释放。用弹力的可塑性、器械的秩序感和移动既视的装置来呈现出空间的模样。有秩序感却又灵活可变的货架本身成为了空间的分割,货架上方的轨道带动着在设定路线下匀速运动的球体装置。
文摘This paper is concerned about studying modeling-based methods in cluster analysis to classify data elements into clusters and thus dealing with time series in view of this classification to choose the appropriate mixed model. The mixture-model cluster analysis technique under different covariance structures of the component densities is presented. This model is used to capture the compactness, orientation, shape, and the volume of component clusters in one expert system to handle Gaussian high dimensional heterogeneous data set. To achieve flexibility in currently practiced cluster analysis techniques. The Expectation-Maximization (EM) algorithm is considered to estimate the parameter of the covariance matrix. To judge the goodness of the models, some criteria are used. These criteria are for the covariance matrix produced by the simulation. These models have not been tackled in previous studies. The results showed the superiority criterion ICOMP PEU to other criteria.<span> </span><span>This is in addition to the success of the model based on Gaussian clusters in the prediction by using covariance matrices used in this study. The study also found the possibility of determining the optimal number of clusters by choosing the number of clusters corresponding to lower values </span><span><span><span>for the different criteria used in the study</span></span></span><span><span><span>.
