A critical review of Mass Estimation & its application in data mining techniques

Mass Estimation, an alternative to density estimation, is proving to be an effective base modeling mechanism in data mining. It is as basic as density estimation which has been the fundamental for most data modeling methods for a wide range of tasks such as classification, clustering and anomaly detection. This paper reviews the theoretical basis of Mass Estimation that can be employed to solve various tasks in data mining and different ways to estimates the mass of data points in different dimensions. The paper also talks about applications of mass estimation in various data mining tasks and their comparison with previously used density estimation technique. The paper reviews the Mass Estimation technique in detail and will be helpful to researchers working in this area, which is relatively new.