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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

LOF is a famous algorithm for outlier detection, and it has lower detection accuracy and higher parameter sensitivity on high-dimensional discrete distribution datasets. Aiming at these problems, this paper proposed a neighborhood system density difference (NSD) algorithm based on density difference measurement of neighborhood systems. Compared with the traditional density-based methods, NSD algorithm proposed and introduced the concept of intercept distance. Firstly, it calculated the number of neighbors of an object within the intercept distance on dataset. Then, it computed the density of neighborhood system. After that, it estimated the degree of tending to the same cluster by comparing the density between the object and its neighbors. Finally, it output the objects which closed to outlier with maximum likelihood. Experiments with NSD, LOF, LDOF, CBOF algorithms carried out on the real-world dataset and synthetic dataset, show that the NSD algorithm performs with higher detection accuracy and execution efficiency, while with lower parameter sensitivity. [ABSTRACT FROM AUTHOR]