[1]
J. Han and M Kamber Data Mining Concepts and Techniques Morgan Kaufman Publishers, (2000).
Google Scholar
[2]
D. Hawkins. Identification of Outliers, Chapman and Hall , (1980).
Google Scholar
[3]
H Liu, W. J., S Shah (2004) On-line outlier detection and data cleaning. Computers and Chemical Engineering, 28, 1635–1647.
DOI: 10.1016/j.compchemeng.2004.01.009
Google Scholar
[4]
H.S Behera, Rosly Boy,Lingdoh, Diptendra Kodama singh An Improved hybridized k -means clustering algorithm for high dimensional data set & it's performance analysis,International Journal on Computer Science and Engineering (IJCSE).
Google Scholar
[5]
M.Vijayalakshmi, M.Renuka Devi" A Survey of different issue of d ifferent clustering algorithms used in large data sets"International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 3,March (2012).
Google Scholar
[6]
Weigend, A. S., Mangeas, M., and Srivastava, A. N. 1995. Nonlinear gated experts for time-series - discovering regimes and avoiding overfitting. International Journal of Neural Systems 6, 4, 373-399.
DOI: 10.1142/s0129065795000251
Google Scholar
[7]
Kou, Y., Lu, C.-T., and Chen, D. 2006. Spatial weighted outlier detection. In Proceedings of SIAM Conference on Data Mining.
DOI: 10.1137/1.9781611972764.71
Google Scholar
[8]
Phoha, V. V. 2002. The Springer Internet Security Dictionary. Springer-Verlag.
Google Scholar
[9]
Wong, W.-K., Moore, A., Cooper, G., and Wagner, M. 2003. Bayesian network outlier pattern detection for disease outbreaks. In Proceedings of the 20th International Conference on Machine Learning. AAAI Press, Menlo Park, California, 808 - 815.
Google Scholar
[10]
Lin, J., Keogh, E., Fu, A., and Herle, H. V. 2005. Approximations to magic: Finding unusual medical time series. In Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems. IEEE Computer Society, Washington, DC, USA, 329 - 334.
DOI: 10.1109/cbms.2005.34
Google Scholar
[11]
J. Lee, J. Han and X. Li. Trajectory Outlier Detection: A Partition-and-Detect Framework. ICDE'08, 140-149,(2008).
Google Scholar
[12]
E. M. Knorr, R. T. Ng and V. Tucakov. Distance-Based Outliers: Algorithms and Applications. VLDB Journal, 8(3-4): 237-253, (2000).
DOI: 10.1007/s007780050006
Google Scholar
[13]
D. Chakrabarti. Autopart: Parameter-free graph partitioning and outlier detection. In PKDD'04, pages 112- 124, (2004).
DOI: 10.1007/978-3-540-30116-5_13
Google Scholar
[14]
C. C. Noble and D. J. Cook. Graph-based anomaly detection. In KDD'03, pages 631-636, (2003).
Google Scholar
[15]
V. Barnett and T. Lewis. Outliers in Statistical Data. John Wiley, 3rd edition, (1994).
Google Scholar
[16]
D. Hawkins. Identification of Outliers. Chapman and Hall, London, (1980).
Google Scholar
[17]
E. Eskin. Anomaly detection over noisy data using learned probability distributions. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML). Morgan Kaufmann Publishers Inc., (2000).
Google Scholar
[18]
Ji Zhang. Advancements of Outlier Detection: A Survey. ICST Transactions on Scalable Information Systems. February (2013).
DOI: 10.4108/trans.sis.2013.01-03.e2
Google Scholar
[19]
P. Murugavel. Performance Evaluation of Density-Based Outlier Detection on High Dimensional Data International Journal on Computer Science and Engineering (IJCSE) Vol. 5 No. 02 Feb (2013).
Google Scholar
[20]
Ritu Ganda. Knowledge Discovery from Database using an Integration of Clustering and Association Rule Mining International Journal of Advanced Research in Computer Science and Software Engineering. Volume 3, Issue 9, September (2013).
Google Scholar
[21]
Ms. S. D. Pachgade, Ms. S. S. Dhande. Outlier Detection over Data Set Using Cluster-Based and Distance-Based Approach. International Journal of Advanced Research in Computer Science and Software Engineering. Volume 2, Issue 6, June (2012).
Google Scholar
[22]
Janpreet Singh, Shruti Aggarwal Survey on Outlier Detection in Data Mining. International Journal of Computer Applications (0975 – 8887) Volume 67– No.19, April (2013).
DOI: 10.5120/11506-7223
Google Scholar
[23]
S. Chawla, Y. Zheng, and J. Hu, Inferring the root cause in road traffic anomalies,, in ICDM, 2012, p.141–150.
Google Scholar
[24]
K. Bache and M. Lichman, UCI machine learning repository,, 2013. [Online]. Available: http://archive.ics.uci.edu/ml.
Google Scholar
[25]
F. Keller, E. Müller, and K. Böhm, HiCS: high contrast subspaces for density-based outlier ranking,, in Proc. ICDE, (2012).
DOI: 10.1109/icde.2012.88
Google Scholar
[26]
A. Zimek, E. Schubert, and H.-P. Kriegel, A survey on unsupervised outlier detection in high-dimensional numerical data,, Stat. Anal. Data Min., vol. 5, no. 5, p.363–387, (2012).
DOI: 10.1002/sam.11161
Google Scholar
[27]
[.Erich Schubert Arthur Zimek Hans-Peter Kriegel.Generalized Outlier Detection with Flexible Kernel Density Estimates. Proceedings of the 14th SIAM International Conference on Data Mining (SDM), Philadelphia, PA, (2014).
DOI: 10.1137/1.9781611973440.63
Google Scholar
[28]
Erich Schubert · Arthur Zimek. Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection.Springer Data Min Knowl Disc (2014).
DOI: 10.1007/s10618-012-0300-z
Google Scholar
[29]
X. H. Dang, I. Assent, R. T. Ng, A. Zimek, E. Schubert. Discriminative Features for Identifying and Interpreting Outliers. In Proceedings of the 30th International Conference on Data Engineering (ICDE), Chicago, IL, (2014).
DOI: 10.1109/icde.2014.6816642
Google Scholar
[30]
A. Zimek, R. J. G. B. Campello, J. Sander Ensembles for Unsupervised Outlier Detection: Challenges and Research Questions ACM SIGKDD Explorations, 15(1): 11–22, (2013).
DOI: 10.1145/2594473.2594476
Google Scholar