Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Science and Technology Print ISSN: 2395-6011 | Online ISSN: 2395-602X (www.ijsrst.com) doi : https://doi.org/10.32628/IJSRST229212 103 An Applied Mean Substitutions Technique for Detection of Anomalous Value in Data Mining Dr. Darshanaben Dipakkumar Pandya 1 , Dr. Abhijeetsinh Jadeja 2 , Dr. Sheshang D. Degadwala 3 1 Assistant Professor, Department of Computer Science, Shri C.J Patel College of Computer Studies (BCA), Visnagar, Gujarat, India 2 Principal(I/C), Department of Computer Science, Shri C.J Patel College of Computer Studies (BCA), Visnagar, Gujarat, India 3 Head of Computer Department, Sigma Institute of Engineering, Vadodara, Gujarat, India Article Info Volume 9, Issue 2 Page Number : 103-108 Publication Issue March-April-2022 Article History Accepted : 01 April 2022 Published : 05 April 2022 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. Keywords: Data Mining, Attribute, Inliers Detection Approach Algorithm, Mean Substitution Technique Algorithm I. INTRODUCTION An anomalous value in database is solitary of the principle problems featured in data analysis and in the prediction. The belongings of these anomalous values are highly reflected on the final results. Our chief goal is to achieve the final result without error in the consolidated form, which is use to take decisions. Now let us consider the following example as a natural occurrence of a physical phenomenon: 0, 0, 0, 0, 0.01, 0.03, 0.08, 1.50, 1.96, 1.21, 1.75, 2.53, 3.90 and 4.10. Here, the first four observations may be treated as instantaneous failures, next three observations may be treated as early failures and other observations may be treated as coming from any failure time distribution. In this study, a method of Inliers detection is introduced and discussed which provides an approach to treat anomalous values. This step treats the anomalous block of values from a real-world imbalanced database.