移动互联网、社交媒体的快速发展,极大推动了各个领域对文本、图像、视频等网络媒体数据处理的需求.该类数据具有高维度、动态更新、内容复杂的特性,增加了特征计算以及分类难度.同时,当前网络媒体数据的特征选择方法主要针对静态数据,...移动互联网、社交媒体的快速发展,极大推动了各个领域对文本、图像、视频等网络媒体数据处理的需求.该类数据具有高维度、动态更新、内容复杂的特性,增加了特征计算以及分类难度.同时,当前网络媒体数据的特征选择方法主要针对静态数据,并且对数据格式规范性要求较高.针对上述问题,为保证对动态网络媒体数据的实时特征提取,该文提出了一种基于用户相关性的动态网络媒体数据无监督特征选择算法(Unsupervised Feature Selection Algorithm for Dynamic Network Media Based on User Correlation,UFSDUC).首先,对社交网络中的交互用户进行关系分析,作为无监督特征选择的约束条件.然后,利用拉普拉斯算子构建用户相关性的特征选择模型,量化相关用户之间的关系强弱,通过拉格朗日乘子法给出特征模型中最优用户关系的数学方法.最后,基于梯度下降法设定动态网络媒体数据的阈值,用以计算非零特征权值来更新最优特征子集,达到对网络媒体数据进行有效分类的目的.该算法可在保证用户在相关性完整的基础上对动态网络媒体数据进行准确、实时的特征选择.该文采用3个标准网络媒体数据集,同时与5种目前较为流行的同类型算法进行对比以验证算法的有效性.展开更多
Visual data mining is one of important approach of data mining techniques. Most of them are based on computer graphic techniques but few of them exploit image-processing techniques. This paper proposes an image proces...Visual data mining is one of important approach of data mining techniques. Most of them are based on computer graphic techniques but few of them exploit image-processing techniques. This paper proposes an image processing method, named RNAM （resemble neighborhood averaging method）, to facilitate visual data mining, which is used to post-process the data mining result-image and help users to discover significant features and useful patterns effectively. The experiments show that the method is intuitive, easily-understanding and effectiveness. It provides a new approach for visual data mining.展开更多
文摘移动互联网、社交媒体的快速发展,极大推动了各个领域对文本、图像、视频等网络媒体数据处理的需求.该类数据具有高维度、动态更新、内容复杂的特性,增加了特征计算以及分类难度.同时,当前网络媒体数据的特征选择方法主要针对静态数据,并且对数据格式规范性要求较高.针对上述问题,为保证对动态网络媒体数据的实时特征提取,该文提出了一种基于用户相关性的动态网络媒体数据无监督特征选择算法(Unsupervised Feature Selection Algorithm for Dynamic Network Media Based on User Correlation,UFSDUC).首先,对社交网络中的交互用户进行关系分析,作为无监督特征选择的约束条件.然后,利用拉普拉斯算子构建用户相关性的特征选择模型,量化相关用户之间的关系强弱,通过拉格朗日乘子法给出特征模型中最优用户关系的数学方法.最后,基于梯度下降法设定动态网络媒体数据的阈值,用以计算非零特征权值来更新最优特征子集,达到对网络媒体数据进行有效分类的目的.该算法可在保证用户在相关性完整的基础上对动态网络媒体数据进行准确、实时的特征选择.该文采用3个标准网络媒体数据集,同时与5种目前较为流行的同类型算法进行对比以验证算法的有效性.
基金Supported by the National Natural Science Foun-dation of China (60173051) ,the Teaching and Research Award Pro-gramfor Outstanding Young Teachers in Higher Education Institu-tions of Ministry of Education of China ,and Liaoning Province HigherEducation Research Foundation (20040206)
文摘Visual data mining is one of important approach of data mining techniques. Most of them are based on computer graphic techniques but few of them exploit image-processing techniques. This paper proposes an image processing method, named RNAM （resemble neighborhood averaging method）, to facilitate visual data mining, which is used to post-process the data mining result-image and help users to discover significant features and useful patterns effectively. The experiments show that the method is intuitive, easily-understanding and effectiveness. It provides a new approach for visual data mining.
