A two level approach to discretize cosmetic data using Rough set theory

Abstract

Discrete values play a very prominent role in extracting knowledge. Most of the machines learning algorithms use discrete values. It is also observed that the rules discovered through discrete values are shorter and precise. The predictive accuracy is more when discrete values are used. Cosmetic industry extracts the features from the face images of the customers to analyze their facial skin problems. These values are continuous in nature. A predictive model with high accuracy is required to determine the cosmetic problems of the customers and suggest suitable cosmetic. Existing traditional discretization techniques are not sufficient for deriving discretized data from continuous valued cosmetic data as it has to balance the loss of information intrinsic to process adapted and generating a reasonable number of cut points, that is, a reasonable search space. This paper proposes a two level discretization method which is a combination of traditional k means clustering technique and rough set theory to discretize continuous features of cosmetic data.