Outlier detection(also known as anomaly detection)is an important research direction in the field of data mining,with the aim of identifying data points that are significantly different.In response to the problem of neglecting the fuzziness and neighborhood relationships of samples in outlier detection methods based on traditional rough set theory,this paper uses fuzzy neighborhood rough sets to compensate for the shortcomings of classical rough sets,and combines the uncertainty of entropy to propose a novel outlier detection method based on fuzzy neighborhood entropy.Firstly,a fuzzy neighborhood approximation space is constructed using fuzzy neighborhood radius and mixed fuzzy similarity.Then,a specific fuzzy neighborhood combination entropy and a relative fuzzy neighborhood combination entropy are defined to construct fuzzy neighborhood outliers.Furthermore,an outlier detection algorithm based on fuzzy neighborhood entropy(FNEOD)was designed by combining the outlier factor based on fuzzy neighborhood combination entropy.Finally,the FNEOD algorithm is compared with the main outlier detection algorithms.The experimental results show that this method has good effectiveness and adaptability.
data miningoutlier detectionfuzzy neighborhood combination entropyrelative fuzzy neighborhood combination entropy
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