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NDoT: Nearest Neighbor Distance Based Outlier Detection Technique

  • Neminath Hubballi
  • Bidyut Kr. Patra
  • Sukumar Nandi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

In this paper, we propose a nearest neighbor based outlier detection algorithm, NDoT. We introduce a parameter termed as \( Nearest \mbox{ } Neighbor\) \(Factor \mbox{ }(NNF)\) to measure the degree of outlierness of a point with respect to its neighborhood. Unlike the previous outlier detection methods NDoT works by a voting mechanism. Voting mechanism binarizes the decision compared to the top-N style of algorithms. We evaluate our method experimentally and compare results of NDoT with a classical outlier detection method LOF and a recently proposed method LDOF. Experimental results demonstrate that NDoT outperforms LDOF and is comparable with LOF.

References

  1. 1.
    Chandola, V., Banerjee, A., Kumar, V.: Outlier detection: A survey. ACM Computing Survey, 1–58 (2007)Google Scholar
  2. 2.
    Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: VLDB 1998: Proceedings of 24th International Conference on Very Large Databases, pp. 392–403 (1998)Google Scholar
  3. 3.
    Angiulli, F., Fassetti, F.: Dolphin: An efficient algorithm for mining distance-based outliers in very large datasets. ACM Transactions and Knowledge Discovery Data 3, 4:1–4:57 (2009)CrossRefGoogle Scholar
  4. 4.
    Breunig, M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: SIGMOD 2000:Proceedings of the 19th ACM SIGMOD international conference on Management of data, pp. 93–104. ACM Press, New York (2000)CrossRefGoogle Scholar
  5. 5.
    Tang, J., Chen, Z., Fu, A.W.-c., Cheung, D.W.: Enhancing Effectiveness of Outlier Detections for Low Density Patterns. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 535–548. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Zhang, K., Hutter, M., Jin, H.: A new local distance-based outlier detection approach for scattered real-world data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 813–822. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. SIGMOD Record 29, 427–438 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Neminath Hubballi
    • 1
  • Bidyut Kr. Patra
    • 2
  • Sukumar Nandi
    • 1
  1. 1.Department of Computer Science & EngineeringIndian Institute of Technology GuwahatiIndia
  2. 2.Department of Computer Science & EngineeringTezpur UniversityTezpurIndia

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