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Unsupervised Distance-Based Anomaly disclosure in RNN

M. Tejasri1 , K. Sri Lakshmi2 , K. Gowri Raghavendra Narayan3

  1. Dept. of CSE, VVIT, Guntur, India.
  2. Dept. of CSE, VVIT, Guntur, India.
  3. Dept. of CSE, VVIT, Guntur, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-3 , Page no. 439-441, Mar-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i3.439441

Online published on Mar 30, 2018

Copyright © M. Tejasri, K. Sri Lakshmi, K. Gowri Raghavendra Narayan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: M. Tejasri, K. Sri Lakshmi, K. Gowri Raghavendra Narayan, “Unsupervised Distance-Based Anomaly disclosure in RNN,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.439-441, 2018.

MLA Style Citation: M. Tejasri, K. Sri Lakshmi, K. Gowri Raghavendra Narayan "Unsupervised Distance-Based Anomaly disclosure in RNN." International Journal of Computer Sciences and Engineering 6.3 (2018): 439-441.

APA Style Citation: M. Tejasri, K. Sri Lakshmi, K. Gowri Raghavendra Narayan, (2018). Unsupervised Distance-Based Anomaly disclosure in RNN. International Journal of Computer Sciences and Engineering, 6(3), 439-441.

BibTex Style Citation:
@article{Tejasri_2018,
author = {M. Tejasri, K. Sri Lakshmi, K. Gowri Raghavendra Narayan},
title = {Unsupervised Distance-Based Anomaly disclosure in RNN},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2018},
volume = {6},
Issue = {3},
month = {3},
year = {2018},
issn = {2347-2693},
pages = {439-441},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1824},
doi = {https://doi.org/10.26438/ijcse/v6i3.439441}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i3.439441}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1824
TI - Unsupervised Distance-Based Anomaly disclosure in RNN
T2 - International Journal of Computer Sciences and Engineering
AU - M. Tejasri, K. Sri Lakshmi, K. Gowri Raghavendra Narayan
PY - 2018
DA - 2018/03/30
PB - IJCSE, Indore, INDIA
SP - 439-441
IS - 3
VL - 6
SN - 2347-2693
ER -

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Abstract

Anomaly discovery in high-dimensional information presents different difficulties coming about because of the "scourge of dimensionality." A common view is that separation fixation, i.e., the propensity of separations in high-dimensional information to wind up garbled, blocks the location of anomalies by making separation based strategies name all focuses as similarly great exceptions. In this paper, we give confirm supporting the conclusion that such a view is excessively straightforward, by exhibiting that separation based strategies can deliver all the more differentiating exception scores in high-dimensional settings. By assessing the great k-NN technique, the density-based local anomaly factor and impacted frameworks strategies, and anti-hub strategies with respect to different manufactured and genuine informational collections, we offer novel knowledge into the value of turn around neighbor checks in unsupervised exception recognition.

Key-Words / Index Term

High-Dimensional Data, Anomaly Detection, Reverse Nearest Neighbors (RNN), Distance Concentration

References

[1] V.Chandola, et al, “Anomaly detection: A survey”, ACM /computSuro, vol 41,no. 3,p. 15,20090
[2] A. Zimek, et al, “A survey on unsupervised outlier detection in high-dimensional numerical data,” Statist. Anal. Data Mining, vol. 5, no. 5, 2012
[3] C. C. Aggarwal et al, “Outlier detection for high dimensional data,” in Proc. 27th ACM SIGMOD Int. Conf. Manage. Data, 2001,
[4] Srinivasa Rao, “A Review on Multivariate Mutual Information”, University of Notre Dame, vol. 2, 2005
[5] Shu Wu, et al, “Information-Theoretic Outlier Detection for Large-Scale Categorical Data”, IEEE Explorer vol. 25, No. 3.
[6] Markus M. et al, “Institute for computer science. Department of Computer Science” University of British Columbia.
[7] A. Hinneburg, et al, “On the surprising behavior of distance metrics in high dimensional spaces,” in Proc 8thIntConf on Database Theory (ICDT), 2001.
[8] Jayshree S.Gosavi, http://www.rroij.com
[9]Random key algorithm https://dzone.com/articles/random-number-generation-in-java
[10]KNN-Algorithm http://www.saedsayad.com/k_nearest_neighbors.htm