Soft Computing Techniques For Categorical Data Analysis In Bio-Informatics

Abstract

Computer based data analytical algorithms have found immense use in all fields. Data Science is emerging as one of the prominent disciplines in Intelligent Bio-Sciences. This field requires collaborative efforts from Biological Scientist, Doctors, Epidemiologists, Computer and Data Scientists, policy makers and administrators. Huge amounts of biological, medical and epidemiological data generated are being studied for knowledge discovery and pattern mining. Knowledge gained from these data are significant as they touch human lives. Hence it is important that they are carefully stored, retrieved, analysed and mined. Categorical (non-numerical data) and high dimensional data are becoming very common in a lot of real-time Bio-Medical applications. These data are packed with information which helps users understand features better as they involve natural language in most cases. But, it is difficult to map the categorical data to scale and analyze where as it is easy in the case of continuous or numerical data. This paper discusses the features of categorical and high dimensional data as well as variants of Fuzzy and Rough set based clustering algorithms like FCM and MMR, MMeR, SDR, SSDR.