Lina Wang1,2,*, Qixiang Zhang1, Xiling Niu1, Yongjun Ren3, Jinyue Xia4
CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1765-1781, 2021, DOI:10.32604/cmc.2021.017516
Abstract Outlier detection is a key research area in data mining technologies, as outlier detection can identify data inconsistent within a data set. Outlier detection aims to find an abnormal data size from a large data size and has been applied in many fields including fraud detection, network intrusion detection, disaster prediction, medical diagnosis, public security, and image processing. While outlier detection has been widely applied in real systems, its effectiveness is challenged by higher dimensions and redundant data attributes, leading to detection errors and complicated calculations. The prevalence of mixed data is a current issue for outlier detection algorithms. An… More >
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