Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outl...Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.展开更多
The Polanyi-Dubinin equation was used to predict adsorption isotherms for eight organic vapors at 25℃ and for two organic vapors at 25℃, 40℃ and 60℃ on activated carbon in this analysis. The theoretical affinity c...The Polanyi-Dubinin equation was used to predict adsorption isotherms for eight organic vapors at 25℃ and for two organic vapors at 25℃, 40℃ and 60℃ on activated carbon in this analysis. The theoretical affinity coefficient, β, was calculated by three methods (molar volume, molecular parachor and electronic polarization) based on nonpolar and polar reference vapors.展开更多
基金Project(61362021)supported by the National Natural Science Foundation of ChinaProject(2016GXNSFAA380149)supported by Natural Science Foundation of Guangxi Province,China+1 种基金Projects(2016YJCXB02,2017YJCX34)supported by Innovation Project of GUET Graduate Education,ChinaProject(2011KF11)supported by the Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education,China
文摘Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.
文摘The Polanyi-Dubinin equation was used to predict adsorption isotherms for eight organic vapors at 25℃ and for two organic vapors at 25℃, 40℃ and 60℃ on activated carbon in this analysis. The theoretical affinity coefficient, β, was calculated by three methods (molar volume, molecular parachor and electronic polarization) based on nonpolar and polar reference vapors.
