The detection of outliers in high-dimensional data raises some of the challenges of “dimension curse”. A major point of view is that the concentration of distances, that is, the distance trends in high-dimensional data becomes illegible, making it difficult to detect outliers by marking all points as values by a distance-based approach. In this paper, implement that the idea of distance-based methods can produce more contrast outliers in high-dimensional environments to provide evidence to support the idea that this view is too simple. In addition, we show that high dimensions can have different effects when there is no oversight to re-examine the concept of a more recent inverse neighbor in the context of atypical detection. It has recently been observed that the distribution of the inverse neighborhood count of points deviates in a high dimension, which causes a phenomenon called a hubness. This work provide information on how some antihubs rarely appear in the k-NN list at other points, and explain the connection between antihubs, outlier values and existing unsupervised outlier detection methods. In evaluating the classical approach to k-NN, angle-based techniques are designed for high-dimensional data, local outliers based on density, and various methods based on anti-sheathing. Combining and real-world data, this work provide new information about the utility of reverse neighborhood counting to detect outliers without supervision.

Clustering, data mining, fuzzy c-means, outliers, unsupervised learning

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