An Applied Variation Anomaly Technique for Detection of Irregular Values in Data Mining

Abstract

Information superiority is significant to organizations. With the use of data mining, Anomalous data values detection is a most important step in many data related applications. Anomalous data make the performance of data analysis difficult. The presence of anomalous data value can also pose serious problems for researchers. In fact, in appropriate handling of the Anomalous data values in the analysis may introduce bias and can result in misleading conclusions being drawn from a research study and can also limit the generalize ability of the research findings. There are numerous techniques for Anomalous data detection, while using Inliers and Outlier techniques and their different measures in data mining. This article introduces anomalous data detection algorithm that should be used in data mining systems. Basic approaches currently used for solving this Anomalous data values finding, problem are considered, and their results are discussed using table.