An outlier detection algorithm based on clustering
Journal Title: Scholars Journal of Engineering and Technology - Year 2015, Vol 3, Issue 6
Abstract
Outlier detection is a very important type of data mining, which is extensively used in application areas. The traditional cell-based outlier detection algorithm not only takes a large amount of time in processing massive data, but also uses lots of machine resources, which results in the imbalance of the machine load. This paper presents an distance-based outlier detection algorithm. These experiments show that this improved algorithm is able to effectively improve the efficiency of the outlier detection as well as the accuracy. Keywords: outlier detection; Euclidean distance; cluster center; maximum distance principle; minimum distance principle
Authors and Affiliations
Hongbo Zhou, Bingbing xu, Juntao Gao
Keywords
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