Ravi Chinapaga, D. Sravya, M Bal Raju, N Subhash Chandra


The interest in outlier is difficult because they include important and practical data in a number of domain names, for example invasion and recognition of fraud in addition to medical diagnosis. It had been in recent occasions observed that distribution of point reverse-neighbour counts become skewed in high dimensions that results within phenomenon acknowledged as hubness. We offer a unifying vision of role concerning reverse nearest neighbour counts within problems relevant to without supervision outlier detection, and concentrate on high dimensionality effects on without supervision outlier-detection techniques additionally to hubness phenomenon. The appearance of anti-hubs is caused by high dimensionality when neighbourhood dimensions are small when in comparison to data size. These anti-hubs occurrence is strongly consort with outlier in high-dimensional in addition to low dimensional data.


Outlier; Hubness; High-Dimensional; Unsupervised; Nearest Neighbour; Outlier -Detection; Anti-Hubs;


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