Cluster analysis for purpose oriented data mining in large databases: Employing new schemas with genetic programming

Kandakatla, Mahesh/ Challa, Lokanatha Reddy

ISBN 10: 6202095717 / ISBN 13: 9786202095716

Published by LAP LAMBERT Academic Publishing, 2017

About this Item

252 pages. 8.66x5.91x0.57 inches. In Stock.

Seller Inventory # zk6202095717

Bibliographic Details

• Title: Cluster analysis for purpose oriented data mining in large databases: Employing new schemas with genetic programming
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 6202095717
• ISBN 13: 9786202095716
• Binding: Paperback
• Condition: Brand New
• Weight: 0.42 kilograms

About this title

Synopsis

Our book attempt to predict the business’ failure or success. A strong point of our book is that, produces perfect solutions compared to other cluster techniques and produced consistently in all runs. The advantage of our book is exploited to decrease the setup time and concentrate on the finding of solutions. It has been confirmed that Genetic Programming can be used to solve real-world problems with less computational time. In this book survival analysis is also used to find the existence of the business/customer. This book discusses a framework for the measurable properties of the QoS delivered to the end-user and QoS provided by the business. Before we come up with time series data mining methods, we itemize which problems need to be tackled. As a general rule, large time series comes along with super-high dimensionality, noise along characteristic patterns, outliers and dynamism. The problems that need to be tackled in time series data mining arise from typical properties of large time series. This book discusses state-of-the-art for QoS frameworks for the business like Telecommunication, retail etc.

About the Author

Mahesh Kandakatla working as associate professor in vaagdevi college of engineering. Completed his Ph.D. in 2016, M.Tech CSE from JNTU Hyderabad; His active research interests include data mining, network security, and ad-hoc networks.