Скачать книгу

      427  430

      428  431

      429  432

      430  433

      431  434

      432  435

      433  436

      434  437

      435  438

      436  439

      437  440

      438  441

      439  442

      440  443

      441  444

      442  445

      443  446

      444  447

      445  448

      446  449

      447  450

      448  451

      449  452

      450  453

      451  454

      452  455

      453  456

      454 457

      455  458

      456  459

      457  461

      458 462

      459 463

      460 464

      461 465

      462  466

      Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106

       Publishers at Scrivener

      Martin Scrivener ([email protected]) Phillip Carmical ([email protected])

      Data Mining and Machine Learning Applications

      Edited by

       Rohit Raja

       Kapil Kumar Nagwanshi

       Sandeep Kumar

      and

       K. Ramya Laxmi

      This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

      © 2022 Scrivener Publishing LLC

      For more information about Scrivener publications please visit www.scrivenerpublishing.com.

      All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

       Wiley Global Headquarters

      111 River Street, Hoboken, NJ 07030, USA

      For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

       Limit of Liability/Disclaimer of Warranty

      While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.

       Library of Congress Cataloging-in-Publication Data

      ISBN 978-1-119-79178-2

      Cover image: Pixabay.Com

      Cover design by Russell Richardson

      Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

      Printed in the USA

      10 9 8 7 6 5 4 3 2 1

      Preface

      Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. However, the term data mining is a misnomer because it means to mine but not extract knowledge. A more apt term would be “knowledge discovery from data,” since it is the practice of examining large pre-existing databases to generate information. Data mining algorithms are currently being investigated and applied worldwide.

      Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification, and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. Data mining algorithms are even used to analyze data by using sentiment analysis. These applications have been increasing in different areas and fields. Web mining and text mining also paved their way to construct the concrete q2 field in data mining.

      This book is intended for industrial and academic researchers, and scientists and engineers in the information technology, data science and machine and deep learning domains. Featured in the book are:

       A

Скачать книгу