Statistical Approaches to Detect Anomalies

  • G. Sandhya MadhuriEmail author
  • M. Usha RaniEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1054)


The term anomaly is derived from a Greek word anomolia meaning uneven or irregular. Anomalies are often referred to as outliers in statistical terminology. For a given set of data if we plot a graph and observe, all the data points that are relative to each other will be plotted densely, whereas some data points which are irrelevant to the data set will be lied away from the rest of the points. We call those points as outliers or anomalies. Anomaly detection is also called as deviation detection, because outlying objects have attribute values that are significantly different from expected or typical attribute values. The anomaly detection is also called as exception mining because anomalies are exceptional in some sense. Anomalous data object is unusual, irregular or in some way, inconsistent with other data objects. In this case, unusual data object or irregular patterns need not be termed as not occurring frequently. If we take a large data set or a continuous data stream, then an unusual data object, that occurs ‘one in a thousand’ times, can occur millions of times in billions of events considered. To find out the anomalies in data sets, we have many approaches like statistical, proximitybased, densitybased and clusterbased. Statistical approaches are model-based approaches where a model is created for the data and objects are calculated with respect to how they are relative with all other objects. In this paper, we will be discussing various statistical approaches to detect anomalies. Most statistical approaches to outlier detection are based on developing a probability distribution model and considering how probable objects are under that model.


Deviation Exceptions Data stream Anomalies Statistical approaches 


  1. 1.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3) (2009) Google Scholar
  2. 2.
    Goldstein, M., Uchida, S.: A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLoS ONE 11(4), 31 (2016)Google Scholar
  3. 3.
    Markou, M., Singh, S.: Novelty detection: a review—part 1: statistical approaches. Sig. Process. 83(12), 2481–2497 (2003)CrossRefGoogle Scholar
  4. 4.
    Hawkins, J., Ahmad, S., Lavin, A.: Biological and Machine Intelligence. Available at: (2016)
  5. 5.
    P-N Tan.: Introduction to “Data Mining”, Michigan State University, Michael Steinbach, University of Minnesota, Vipin Kumar, University of Minnesota and Army High Performance Computing Research CenterGoogle Scholar
  6. 6.
  7. 7.
    Song, X., Wu, M., Jermaine, C.: Conditional anomaly detection. IEEE Trans. Knowl. Data Eng. 19(5), 631–645 (2007)Google Scholar
  8. 8.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceSPMVVTirupatiIndia

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