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Your search keyword '"Xiong, Zhongyang"' showing total 36 results
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36 results on '"Xiong, Zhongyang"'

1. A New Density Clustering Method Using Mutual Nearest Neighbor

Author

Zhang, Yufang, Zha, Yongfang, Li, Lintao, Xiong, Zhongyang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, U, Leong Hou, editor, Spaniol, Marc, editor, Sakurai, Yasushi, editor, and Chen, Junying, editor

Published
2021
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11. Intrusion Detection Based on Dynamic Self-organizing Map Neural Network Clustering

Author

Feng, Yong, Wu, Kaigui, Wu, Zhongfu, Xiong, Zhongyang, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Jun, editor, Liao, Xiao-Feng, editor, and Yi, Zhang, editor

Published
2005
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17. 薄片型红外/被动毫米波复合诱饵的研究

Author

熊钟洋 Xiong Zhongyang, 朱晨光 Zhu Chenguang, 端木凡顺 Duanmu Fanshun, and 李经纬 Li Jingwei

Subjects
Space and Planetary Science, Aerospace Engineering, Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics
Published
2022

31. Effectiveness of the Euclidean distance in high dimensional spaces.

Author

Xia, Shuyin, Xiong, Zhongyang, Luo, Yueguo, WeiXu, null, and Zhang, Guanghua

Subjects
*EUCLIDEAN distance, *RANDOM variables, *STOCHASTIC convergence, *DISTRIBUTION (Probability theory), *TOPOLOGICAL spaces
Abstract

This paper presents analysis of applicability and performance of the Euclidean distance in relation to the dimensionality of the space. The effect of dimensionality on the behavior of Euclidean distance is explored; Furthermore, it is shown that the minimum distance approaches the maximum distance under a broader set of conditions without requiring the calculation of variance of random variables. It is demonstrated that the minimum distance approaches the maximum distance even for some low dimensional distributions, such as normal distribution. Many proposed measures not based directly on Euclidean distance cannot enlarger the difference between closest point and farthest point. The analysis has been performed on a wide range of artificial and publicly available datasets. As the variables of different distributions have different convergence rates, the results should not be interpreted to mean that the Euclidean distance is not applicable. In fact, it is shown in experiments that the Euclidean distance is very useful in the noncentral t -distribution even for the dimensionality higher than 10,000. Furthermore, it is observed that the behavior of Euclidean distance becomes more useful with increased number of samples. [ABSTRACT FROM AUTHOR]

Published
2015
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34. Intrusion Detection Based on Dynamic Self-organizing Map Neural Network Clustering.

Author

Wang, Jun, Liao, Xiaofeng, Yi, Zhang, Feng, Yong, Wu, Kaigui, Wu, Zhongfu, and Xiong, Zhongyang

Abstract

An approach to network intrusion detection is investigated, based on dynamic self-organizing maps (DSOM) neural network clustering. The basic idea of the method is to produce the cluster by DSOM. With the classified data instances, anomaly data clusters can be easily identified by normal cluster ratio. And then the identified cluster can be used in real data detection. In the traditional clustering-based intrusion detection algorithms, clustering using a simple distance-based metric and detection based on the centers of clusters, which generally degrade detection accuracy and efficiency. Our approach based on DSOM clustering can settle these problems effectively. The experiment result shows that our approach can detect unknown intrusions efficiently in the real network connections. [ABSTRACT FROM AUTHOR]

Published
2005
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