Cluster Computing

, Volume 22, Supplement 6, pp 13177–13183 | Cite as

A scheme for detecting outliers using sequential adjacency among entities

  • V. KathiresanEmail author
  • N. A. Vasanthi


Presently the outlier extraction is broadly employed in applications such as communication, health care, finance and network-based interference identification. The use of this outlier extraction in network irregularity identification, occasionally happening assaults could be recognized. The intention is to design an outlier extraction scheme in terms of sequential adjacency associations. The evaluations are performed against the UCIML information set, KDD and real-time information sets. The analysis revealed that the outcomes are offering better outcomes than the prevailing schemes.


Outlier extraction Irregularities Associations Interferences and Sequential Adjacency Associations 


  1. 1.
    Wang, Y., Li, J., Wang, H.H.: Cluster and cloud computing framework for scientific metrology in flow control. Cluster Comput. (2017). CrossRefGoogle Scholar
  2. 2.
    Zhang, S., Wang, H., Huang, W.: Two-stage plant species recognition by local mean clustering and weighted sparse representation classification. Cluster Comput 20, 1517–1525 (2017)CrossRefGoogle Scholar
  3. 3.
    Lohiya, S., Ramayi, P., Pillai, A., Veturi, S.: Mar. Privacy feedback system using data mining and outlier detection algorithm. Int. J. Innov. Res. Comput. Sci. Commun. Eng. 5(3), 4356–4363 (2017)Google Scholar
  4. 4.
    Biering-Sørensen, T., Jensen, J.S., Pedersen, S.H., Galatius, S., Fritz-Hansen, T., Bech, J., Olsen, F.J., Mogelvang, R.: Regional longitudinal myocardial deformation provides incremental prognostic information in patients with ST-segment elevation myocardial infarction. PloS ONE 11(6), e0158280 (2016)CrossRefGoogle Scholar
  5. 5.
    Wang, H., Wang, J.: November: an effective image representation method using kernel classification. In: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 853–858. IEEE (2014)Google Scholar
  6. 6.
    Kim, H.Y., Oh, G.: Analysis of information flows among individual companies in the KOSDAQ market. J. Korean Phys. Soc. 69(4), 455–460 (2016)CrossRefGoogle Scholar
  7. 7.
    Mashkina, I.V., Guzairov, M.B., Vasilyev, V.I., Tuliganova, L.R., Konovalov, A.S.: Issues of information security control in virtualization segment of company information system. In: 2016 XIX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 161–163. IEEE (2016)Google Scholar
  8. 8.
    Agarwal, A., Solanki, A.: An improved data clustering algorithm for outlier detection. Int. Acad. Ecol. Environ. Sci. 3(4), 121–139 (2016)Google Scholar
  9. 9.
    Singh, A.A.G., Leavline, J.: Model based outlier detection system with statistical pre-processing. J. Mod. Appl. Stat. Method 15(1), 789–801 (2016)CrossRefGoogle Scholar
  10. 10.
    Chauhan, P., Shukla, M.: A review on outlier detection techniques on data stream by using different approaches of K-means algorithm. In: International Conference on Advances in Computer Engineering and Applications (2015)Google Scholar
  11. 11.
    Kamble, B., Doke, K.M.: Outlier detection approaches in data mining. Int. Res. J. Eng. Technol. 4(3), 634–638 (2017)Google Scholar
  12. 12.
    Kaur, P., Kaur, K.: A review on outlier detection for data cleaning in data mining. Int. J. Innov. Res. Comput. Commun. Eng. 4(7) (2016)Google Scholar
  13. 13.
    Pawar, P., Ghuse, N.: Data mining techniques for fraud detection in health insurance. Int. J. Inform. Futur. Res. 4(5), 6404–6410 (2017)Google Scholar
  14. 14.
    Kureshi, M.N., Abidi, S.S.R.: A predictive model for personalized therapeutic interventions in non-small cell lung cancer. IEEE J. Health Inform. 20(1), 424–431 (2016)CrossRefGoogle Scholar
  15. 15.
    Kaur, P., Parmjeet, K.: An overview of data mining tools. Int. J. Eng. Appl. Sci. Technol. 1(6), 41–46 (2016)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringCoimbatore Institute of Engineering and TechnologyCoimbatoreIndia
  2. 2.Department of Information TechnologyDr.N.G.P Institute of TechnologyCoimbatoreIndia

Personalised recommendations