Scalable Non-Parametric Pattern Recognition Techniques for Data Mining

Knowledge discovery in data is called data mining. Many data mining techniques require classification and clustering. Large data sets are available nowadays in the world and fast approaches of classification or clustering becomes a tedious work with large data sets. For example, computer vision, text mining, semantic web mining, natural language processing etc., require non-parametric pattern recognition methods. This book describes fast approaches to discover knowledge from large data sets.This book deals with condensing of large data and also preserving essential information in the data. This book describes many efficient fast classifiers and clustering methods which are based on density information in the large data sets. It describes to resolve vagueness and uncertainty that is present in large data sets using combination principles of rough sets and fuzzy sets. Approaches in this book are adaptive and they can be applied in many machine learning methods in many domains.

Autorentext

Dr. Suresh Veluru is a postdoctoral fellow at University of New Brunswick,Canada.He received his PhD from Indian Institute of Technology Guwahati, India in 2009. Dr. P. Viswanath is a Professor and Dean R&D (Electrical Sciences) at Rajeev Gandhi Memorial College of Eng. & Tech., Nandyal. He received his PhD from IISc, Bangalore,India.