Intelligent Techniques for Data Analysis in Diverse Settings (Advances in Data Mining and Database Management)

ISBN 10: 1522500758 / ISBN 13: 9781522500759

Published by Information Science Reference, 2016

About this Item

Seller Inventory # I-9781522500759

Bibliographic Details

• Title: Intelligent Techniques for Data Analysis in Diverse Settings (Advances in Data Mining and Database Management)
• Publisher: Information Science Reference
• Publication Date: 2016
• Language: English
• ISBN 10: 1522500758
• ISBN 13: 9781522500759
• Binding: Hardcover
• Condition: New

About this title

Synopsis

Data analysis forms the basis of many forms of research ranging from the scientific to the governmental. With the advent of machine intelligence and neural networks, extracting, modeling, and approaching data has been unimpeachably altered. These changes, seemingly small, affect the way societies organize themselves, deliver services, or interact with each other. Intelligent Techniques for Data Analysis in Diverse Settings addresses the specialized requirements of data analysis in a comprehensive way. This title contains a comprehensive overview of the most innovative recent approaches borne from intelligent techniques such as neural networks, rough sets, fuzzy sets, and metaheuristics. Combining new data analysis technologies, applications, emerging trends, and case studies, this publication reviews the intelligent, technological, and organizational aspects of the field. This book is ideally designed for IT professionals and students, data analysis specialists, healthcare providers, and policy makers.

About the Author

Numan Çelebi received his M.Sc.in electrical engineer from Istanbul Technical University, Turkey in 1989. He received his Ph.D. in Industrial Engineering from Sakarya University, Turkey, in 2004. He was in Auburn University, USA in 2009-2011 as a visiting scholar funded by TUBITAK, Turkey. His current research interest in in the area of data mining on rough set theory. In addition, Dr. Çelebi is interested in the area of metaheuristic algorithms and their applications in computer science.