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 outlier detection algorithm based on maximum and minimum distance. These experiments show that the improved algorithm is able to effectively improve the efficiency of the outlier detection as well as the accuracy.
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