An Applied Mean Substitutions Technique for Detection of Anomalous Value in Data Mining

Authors

  • Dr. Darshanaben Dipakkumar Pandya  Assistant Professor, Department of Computer Science, Shri C.J Patel College of Computer Studies (BCA), Visnagar, Gujarat, India
  • Dr. Abhijeetsinh Jadeja  Principal(I/C), Department of Computer Science, Shri C.J Patel College of Computer Studies (BCA), Visnagar, Gujarat, India
  • Dr. Sheshang D. Degadwala  Head of Computer Department, Sigma Institute of Engineering, Vadodara, Gujarat, India

DOI:

https://doi.org/10.32628/IJSRST229212

Keywords:

Data Mining, Attribute, Inliers Detection Approach Algorithm, Mean Substitution Technique Algorithm

Abstract

In the numerical value database, inliers in a database are subset of observations adequately small enough compared to the rest of the observations, which appears to be inconsistent with the remaining data set. They are the result of instant failures or early failures, experienced in many life-test experiments. The problem is how to handle Inliers in a dataset, and how to evaluate the Inliers. This paper describes a revolutionary of approach that uses Inliers detection as a pre-processing step to detect the Inliers and then applies Mean Substitution technique algorithm, hence to analyze the effects of the Inliers on the analysis of dataset.

References

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International Journal of Scientific Research in Science and Technology

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Published

2022-04-30

Issue

Volume 9, Issue 2, March-April-2022

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. Darshanaben Dipakkumar Pandya, Dr. Abhijeetsinh Jadeja, Dr. Sheshang D. Degadwala "An Applied Mean Substitutions Technique for Detection of Anomalous Value in Data Mining " International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 2, pp.103-108, March-April-2022. Available at doi : https://doi.org/10.32628/IJSRST229212