Fragmented Central Affinity Approach for Reducing Ambiguities in Dataset

Abstract

It is always a known fact that the role of data and its purity is very crucial in the data mining. The key role of data in the data mining is related from decision-making. It is well-known fact that if data are impure, then result will be a false picture. This crucial stage is also known as the ambiguities in datasets. Anomalous or irregular value in database is solitary of the biggest problems faced in data analysis and in data mining applications. Data preprocessing for the data mining is a key phase which is crucial place where ambiguities of database can be reduce or remove. The present study proposed an algorithm which tries to solve the problem related to an anomalous and irregular values, i.e., outliers, inliers, and missing values from a real-world imbalanced database. The study projected is based on the fragmented central affinity approach for reducing ambiguities in dataset.