Anomaly Detection Using Data Mining Methods
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2019 by IJCTT Journal|
|Year of Publication : 2019|
|Authors : S.Sreekanth, Dr. P.C.Rao|
|DOI : 10.14445/22312803/IJCTT-V67I12P105|
MLA Style:S.Sreekanth, Dr. P.C.Rao "Anomaly Detection Using Data Mining Methods" International Journal of Computer Trends and Technology 67.12 (2019):20-23.
APA Style S.Sreekanth, Dr. P.C.Rao. Anomaly Detection Using Data Mining Methods International Journal of Computer Trends and Technology, 67(12),20-23.
Anomaly is characterized as an occasion that veers off a lot from different occasions. The recognizable proof of anomaly can prompt the disclosure of valuable and important Data. Anomaly implies it`s occur eventually it`s not customary movement. Research about Detection of Outlier has been widely ponders in the previous decade. In any case, most existing examination concentrated on the calculation dependent on explicit Data, contrasted and anomaly discovery approach is as yet uncommon. In this paper fundamentally centered around various sort of exception identification approaches and thinks about it`s inclined and cones. In this paper we primarily disperse of exception recognition approach in two sections exemplary anomaly approach and spatial exception approach. The old style exception approach recognizes anomaly in genuine exchange dataset, which can be assembled into measurable methodology, separation approach, deviation approach, and thickness approach. The spatial exception approach identify anomaly dependent on spatial dataset are not quite the same as exchange Data, which can be classified into divided methodology and chart approach. At long last, the correlation of anomaly discovery draws near.
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