This is an indispensable scientific text book brings the beginners right to the forefront of the in-depth fundamentals of the traditional data mining classification techniques for the analysis of the neuro-degenerative disease with special reference to the Alzheimer's Disease. Beginning with an overview of the alzheimer's disease, this text book analyses the bio markers and the classification techniques in detail. Then follows an in-depth treatment of the better literature coverage. This book is ideally suited for the researchers of Computer Science and the researchers will benefit from the comprehensive wide range of literature coverage and the better explanations on cutting-edge material.

At the highest level of description, this book is about dimensionality reduction for data mining. It focuses on dimensional reduction on Small, Medium and very large Scale data. This book is not intended as an introduction to Feature selection, although we do provide the background necessary in several areas in order to facilitate the reader's comprehension of their respective roles in data mining. This book reviews state-of-the-art dimensionality reduction techniques for analyzing enormous quantities of raw data in high-dimensional data spaces to extract new information useful to the decision-making process.

Feature selection is a term commonly used in data mining to describe the tools and techniques available for reducing inputs to a manageable size for processing and analysis. Feature selection implies not only cardinality reduction, which means imposing an arbitrary or predefined cutoff on the number of attributes that can be considered when building a model, but also the choice of attributes, meaning that either the analyst or the modeling tool actively selects or discards attributes based on their usefulness for analysis. "Feature selection methods best Practices" is the mast reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and research scholars.