Data Mining and Machine Learning Applications E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 653

Veröffentlichungsjahr: 2022

Beschreibung

DATA MINING AND MACHINE LEARNING APPLICATIONS

The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration.

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.

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.

The book features:

• A review of the state-of-the-art in data mining and machine learning,
• A review and description of the learning methods in human-computer interaction,
• Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time,
• The scope and implementation of a majority of data mining and machine learning strategies.
• A discussion of real-time problems.

Audience

Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Preface

1 Introduction to Data Mining

1.1 Introduction

1.2 Knowledge Discovery in Database (KDD)

1.3 Issues in Data Mining

1.4 Data Mining Algorithms

1.5 Data Warehouse

1.6 Data Mining Techniques

1.7 Data Mining Tools

References

2 Classification and Mining Behavior of Data

2.1 Introduction

2.2 Main Characteristics of Mining Behavioral Data

2.3 Research Method

2.4 Results

2.5 Discussion

2.6 Conclusion

References

3 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects

3.1 Introduction

3.2 Related Work on Different Recommender System

References

4 Stream Mining: Introduction, Tools & Techniques and Applications

4.1 Introduction

4.2 Data Reduction: Sampling and Sketching

4.3 Concept Drift

4.4 Stream Mining Operations

4.5 Tools & Techniques

4.6 Applications

4.7 Conclusion

References

5 Data Mining Tools and Techniques: Clustering Analysis

5.1 Introduction

5.2 Data Mining Task

5.3 Data Mining Algorithms and Methodologies

5.4 Clustering the Nearest Neighbor

5.5 Data Mining Applications

5.6 Materials and Strategies for Document Clustering

5.7 Discussion and Results

References

6 Data Mining Implementation Process

6.1 Introduction

6.2 Data Mining Historical Trends

6.3 Processes of Data Analysis

References

7 Predictive Analytics in IT Service Management (ITSM)

7.1 Introduction

7.2 Analytics: An Overview

7.3 Significance of Predictive Analytics in ITSM

7.4 Ticket Analytics: A Case Study

7.5 Conclusion

References

8 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques

8.1 Introduction

8.2 Literature Review

8.3 Methodology and Implementation

8.4 Data Partitioning

8.5 Conclusions

References

9 Inductive Learning Including Decision Tree and Rule Induction Learning

9.1 Introduction

9.2 The Inductive Learning Algorithm (ILA)

9.3 Proposed Algorithms

9.4 Divide & Conquer Algorithm

9.5 Decision Tree Algorithms

9.6 Conclusion and Future Work

References

10 Data Mining for Cyber-Physical Systems

10.1 Introduction

10.2 Feature Recovering Methodologies

10.3 CPS vs. IT Systems

10.4 Collections, Sources, and Generations of Big Data for CPS

10.5 Spatial Prediction

10.6 Clustering of Big Data

10.7 NoSQL

10.8 Cyber Security and Privacy Big Data

10.9 Smart Grids

10.10 Military Applications

10.11 City Management

10.12 Clinical Applications

10.13 Calamity Events

10.14 Data Streams Clustering by Sensors

10.15 The Flocking Model

10.16 Calculation Depiction

10.17 Initialization

10.18 Representative Maintenance and Clustering

10.19 Results

10.20 Conclusion

References

11 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining

11.1 Introduction

11.2 Background

11.3 Methodology of CRISP-DM

11.4 Stage One—Determine Business Objectives

11.5 Stage Two—Data Sympathetic

11.6 Stage Three—Data Preparation

11.7 Stage Four—Modeling

11.8 Stage Five—Evaluation

11.9 Stage Six—Deployment

11.10 Data on ERP Systems

11.11 Usage of CRISP-DM Methodology

11.12 Modeling

11.13 Assessment

11.14 Distribution

11.15 Results and Discussion

11.16 Conclusion

References

12 Human–Machine Interaction and Visual Data Mining

12.1 Introduction

12.2 Related Researches

12.3 Visual Genes

12.4 Visual Hypotheses

12.5 Visual Strength and Conditioning

12.6 Visual Optimization

12.7 The Vis 09 Model

12.8 Graphic Monitoring and Contact With Human–Computer

12.9 Mining HCI Information Using Inductive Deduction Viewpoint

12.10 Visual Data Mining Methodology

12.11 Machine Learning Algorithms for Hand Gesture Recognition

12.12 Learning

12.13 Detection

12.14 Recognition

12.15 Proposed Methodology for Hand Gesture Recognition

12.16 Result

12.17 Conclusion

References

13 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection

13.1 Introduction

13.2 Literature Survey

13.3 Methods and Material

13.4 Experimental Results

13.5 Libraries Used

13.6 Comparing Algorithms Based on Decision Boundaries

13.7 Evaluating Results

13.8 Conclusion

References

14 New Algorithms and Technologies for Data Mining

14.1 Introduction

14.2 Machine Learning Algorithms

14.3 Supervised Learning

14.4 Unsupervised Learning

14.5 Semi-Supervised Learning

14.6 Regression Algorithms

14.7 Case-Based Algorithms

14.8 Regularization Algorithms

14.9 Decision Tree Algorithms

14.10 Bayesian Algorithms

14.11 Clustering Algorithms

14.12 Association Rule Learning Algorithms

14.13 Artificial Neural Network Algorithms

14.14 Deep Learning Algorithms

14.15 Dimensionality Reduction Algorithms

14.16 Ensemble Algorithms

14.17 Other Machine Learning Algorithms

14.18 Data Mining Assignments

14.19 Data Mining Models

14.20 Non-Parametric & Parametric Models

14.21 Flexible vs. Restrictive Methods

14.22 Unsupervised vs. Supervised Learning

14.23 Data Mining Methods

14.24 Proposed Algorithm

14.25 The Regret of Learning Phase

14.26 Conclusion

References

15 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier

15.1 Introduction

15.2 Related Work

15.3 Material and Methods

15.4 Experimental Framework

15.5 Experimental Results and Discussion

15.6 Discussion

15.7 Conclusion

References

16 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques

16.1 Introduction

16.2 Related Work

16.3 Issue and Solution

16.4 Selection of Data

16.5 Pre-Preparation Data

16.6 Application Development

16.7 Use Case For The Application

16.8 Conclusion

References

17 Conclusion and Future Direction in Data Mining and Machine Learning

17.1 Introduction

17.2 Machine Learning

17.3 Conclusion

References

Index

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1 Knowledge discovery in Database—KDD.

Figure 1.2 Time series database.

Figure 1.3 Data warehouse.

Figure 1.4 Decision Tree.

Figure 1.5 Installation of KNIME.

Figure 1.6 Installation of KNIME (2).

Figure 1.7 Setting path for installing KNIME.

Figure 1.8 Starting installation of KNIME.

Figure 1.9 Selecting directory as a workspace.

Figure 1.10 Starting KNIME.

Figure 1.11 Completing setup wizard.

Figure 1.12 Installing Workspace in KNIME.

Figure 1.13 Installing KNIME (2).

Figure 1.14 Specifying memory for KNIME.

Figure 1.15 Finalizing the installation of KNIME.

Figure 1.16 Initial screen of KNIME.

Chapter 2

Figure 2.1 Process of mining data stream.

Figure 2.2 Sample of graph data set.

Figure 2.3 Multi-source & multidimensional information.

Figure 2.4 Shows the data mining process of multimedia data.

Figure 2.5 Mining of multimedia data.

Figure 2.6 Shows social media mining.

Figure 2.7 Shows total facebook users per year.

Figure 2.8 Shows the spatiotemporal data mining process.

Figure 2.9 Shows the schematic outline of the information mining-based strategy.

Figure 2.10 Calculated regression output.

Figure 2.11 Highlight significance yield.

Figure 2.12 Cause patterns of ventilation system operations.

Chapter 3

Figure 3.1 Evolution process of RS.

Figure 3.2 Recommender systems.

Figure 3.3 Demographic recommender systems.

Figure 3.4 Utility recommender system.

Figure 3.5 Knowledge-Based RS.

Figure 3.6 Hybrid recommender system.

Figure 3.7 Architecture of this paper.

Chapter 4

Figure 4.1 Overview of data stream processing system.

Figure 4.2 Configuration settings for stream generators in MOA.

Figure 4.3 Learning models for various classifiers in MOA.

Figure 4.4 Performance Parameters for Evaluating Classifiers in MOA.

Figure 4.5 Clustering window of MOA for data stream clustering.

Figure 4.6 Clustering algorithms available in MOA.

Figure 4.7 Performance parameters for evaluating clustering in MOA.

Figure 4.8 Clustering visualization in MOA.

Figure 4.9 Concept drift detection techniques in MOA.

Figure 4.10 Active learning algorithms in MOA.

Figure 4.11 Outlier detection algorithms available in MOA.

Figure 4.12 Outlier visualization in MOA.

Figure 4.13 Code snippet for demonstrating stream package from R for stream clus...

Figure 4.14 Output of stream package from R for stream clustering.

Figure 4.15 Code snippet for clustering animation from stream package in R.

Figure 4.16 Clustering animation from stream package in R.

Chapter 5

Figure 5.1 Shows different data clustering stages.

Figure 5.2 Shows clustering techniques classifications.

Figure 5.3 Shows centroid linkage clustering.

Figure 5.4 Show fuzzy clustering.

Figure 5.5 Shows silhouette’s graphical representation of clusters. (a) Represen...

Figure 5.6 Shows Output vectors of Algorithm.

Figure 5.7 Shows realistic of the three Silhouettes with a various number of gro...

Figure 5.8 Shows Silhouette estimation using K-Means Algorithm archives by the f...

Chapter 6

Figure 6.1 Data mining implementation process.

Figure 6.2 Shows flowchart of the research.

Figure 6.3 Exactness classifier’s comparison.

Figure 6.4 Datastream model.

Figure 6.5 Model execution graph.

Chapter 7

Figure 7.1 Process flow of incident and ticket.

Figure 7.2 Process flow of incident and ticket.

Figure 7.3 Service request effort estimation and incident resolution workflow.

Figure 7.4 Ticket count vs. day of week based on priority of the tickets.

Figure 7.5 Ticket count vs time of the day in which it is logged based on priori...

Figure 7.6 Industry domain by ticket volume.

Figure 7.7 Sample ticket format.

Figure 7.8 Raw data collected from organization.

Figure 7.9 Research methodology to develop effort prediction model.

Figure 7.10 Effort values predicted vs. observed using training and test dataset...

Figure 7.11 Effort values predicted vs. observed using training and test dataset...

Chapter 8

Figure 8.1 Process life cycle.

Figure 8.2 Confusion matrices.

Chapter 9

Figure 9.1 Decision tree example.

Figure 9.2 Shows the ID3 algorithm.

Figure 9.3 Shows RULES flow chart.

Figure 9.4 RULES-3 calculation.

Figure 9.5 Procedure of RULE-3 plus rule forming.

Figure 9.6 RULE-4 incremental induction procedure.

Figure 9.7 Pseudocode portrayal of RULES-6.

Figure 9.8 RULES3-EXT calculation.

Figure 9.9 A disentangled depiction of RULES-7.

Figure 9.10 REX-1 algorithm.

Figure 9.11 Construction procedure of fuzzy decision tree.

Figure 9.12 Offered multidimensional databases architecture from fuzzy data mini...

Chapter 10

Figure 10.1 Shows different sources of big data.

Figure 10.2 Show automated CPS cycle.

Figure 10.3 Show clustering of big data.

Figure 10.4 Shows CPS smart grid.

Figure 10.5 Shows CPS military application.

Figure 10.6 Shows CPS smart city application.

Figure 10.7 Shows CPS environmental application.

Figure 10.8 FlockStream Algorithm’s pseudo-code.

Figure 10.9 (a) Synthetic information sets. (b) Clustering was performed by Floc...

Chapter 11

Figure 11.1 Shows CRISP-DM methodology.

Figure 11.2 Shows all six stages of the CRISP-DM model.

Figure 11.3 Shows business understanding phase of CRISP-DM model.

Figure 11.4 Shows data understanding phase of CRISP-DM model.

Figure 11.5 Shows data preparation phase of CRISP-DM model.

Figure 11.6 Shows modeling phase of CRISP-DM model.

Figure 11.7 Shows evaluation phase of the CRISP-DM model.

Figure 11.8 Shows deployment phase of CRISP-DM model.

Figure 11.9 Shows coordination of various modules in ERP frameworks.

Figure 11.10 Cash flow data mining CRISP-DM methodology.

Figure 11.11 InfoCube loading in SAP BIW’s ETL mapping.

Figure 11.12 DM modeling & visualization of SAP ADP.

Figure 11.13 Shows overall influence chart of relative dominance in clustering m...

Figure 11.14 Overall influence chart of clustering model.

Chapter 12

Figure 12.1 Shows the CRISP and KDD process flowchart.

Figure 12.2 Shows data mining process.

Figure 12.3 Shows visual analytics flowchart.

Figure 12.4 Shows visual analytics and human–interaction.

Figure 12.5 HCI information mining approach with an accentuation on parts of ind...

Figure 12.6 Reflections of basic inductive learning ideas thought about and rela...

Figure 12.7 Shows human involvement in different data mining approaches.

Figure 12.8 Flowchart of gesture recognition.

Figure 12.9 Block diagram of Gesture Recognition framework.

Figure 12.10 (a, b, c, d): Shows hand gestures.

Figure 12.11 Zoom-in gesture recognized.

Figure 12.12 Zoom-out gesture recognized.

Figure 12.13 Towards right movement gesture recognized.

Chapter 13

Figure 13.1 Different types of datasets: (a) standard (balanced), (b) unbalanced...

Figure 13.2 Sample skin lesion images from public dataset (a) PH2, (b) ISIC2016,...

Figure 13.3 Illustration of proposed framework.

Figure 13.4 Representing decision boundary amid negative and positive instances ...

Figure 13.5 Performance comparison analysis. (a) AUCROC respective to standard d...

Chapter 14

Figure 14.1 Shows supervised learning algorithm.

Figure 14.2 Shows unsupervised learning algorithm.

Figure 14.3 Shows semi-supervised learning algorithm.

Figure 14.4 Shows regression learning algorithm.

Figure 14.5 Shows instance-bases learning algorithm.

Figure 14.6 Shows regularization algorithm.

Figure 14.7 Shows decision-tree algorithm.

Figure 14.8 Shows bayesian algorithm.

Figure 14.9 Shows clustering algorithm.

Figure 14.10 Shows association rule learning algorithm.

Figure 14.11 Shows artificial neural network algorithm.

Figure 14.12 Shows deep learning algorithm.

Figure 14.13 Shows dimensionality reduction algorithm.

Figure 14.14 Shows ensemble algorithm.

Figure 14.15 Information mining assignments and models.

Figure 14.16 Descriptions of under-fit, standard, and over-fit versions.

Figure 14.17a Shows supervised learning.

Figure 14.17b Shows sigmoid function.

Figure 14.18 Shows one-versus all or multi-class classification.

Figure 14.19 Shows illustration of clustering process.

Figure 14.20 The description of the learning phase.

Figure 14.21 Harvard database result.

Figure 14.22 Sub-part result of

Iris setosa

.

Figure 14.23 Sub-part result of iris versicolour.

Figure 14.24 Sub-part result of

Iris virginica

.

Chapter 15

Figure 15.1 Represents the flow chart of proposed methodology using three layers...

Figure 15.2 Flow diagram of PCA algorithm used in our proposed methodology.

Figure 15.3 Simple architecture of 3-Layer RBM.

Figure 15.4 Performance Metric Comparison (Accuracy) by using 80–20 as size rati...

Figure 15.5 Performance Metric Comparison (Sensitivity) by using 80–20 as size r...

Figure 15.6 Performance Metric Comparison (Specificity) by using 80–20 as size r...

Figure 15.7 The seizure occurs during each hour for all the pediatric patients o...

Chapter 16

Figure 16.1 Option of boundary space measurements for the Delhi database. (a) wi...

Figure 16.2 Selection of boundary for worldwide calculation of the Delhi dataset...

Figure 16.3 Shows heatmap.

Figure 16.4 Example of transformation from a heatmap into a double guide utilizi...

Figure 16.5 Binary anticipated guides utilizing various percentiles to character...

Figure 16.6 Label forecast by characterized percentile edge.

Figure 16.7 Model evaluation against different category levels. It is feasible t...

Figure 16.8 Heatmaps assumptions using several edges. (a) uses the 96th percenti...

Figure 16.9 Large-goal heatmap from over Delhi region. The rose-colored region o...

Figure 16.10 Shows flow chart of proposed application system.

Figure 16.11 Shows icon of application.

Figure 16.12 Shows form of registration.

Figure 16.13 Shows login page.

Figure 16.14 Misconduct place finder.

Figure 16.15 Location recognized on map.

Figure 16.16 Show message sent by user.

Figure 16.17 Shows received message to enrolled contact.

Figure 16.18 Location of client.

Chapter 17

Figure 17.1 Object instance segmentation.

List of Tables

Chapter 1

Table 1.1 Comparison in a data warehouse—OLTP.

Chapter 2

Table 2.1 Shows social media network applies to various data services.

Chapter 3

Table 3.1 Show the advantage and disadvantage of different types of RS.

Table 3.2 Shows which technique, evaluation criteria are used in different RS.

Table 3.3 Base of usage predication.

Table 3.4 Comparative study of hybrid approach with traditional approach.

Chapter 5

Table 5.1 The transformation from word to lexeme.

Table 5.2 Transformation to an extraordinary word list.

Table 5.3 Vector portrayal of a report corpus.

Table 5.4 The portrayal of the corpus utilized in this examination.

Table 5.5 Groups with various Silhouette an incentive for every calculation.

Chapter 6

Table 6.1 Data mining developments qualified statement.

Table 6.2 Shows application and usage of data mining.

Table 6.3 Understudy related factors.

Table 6.4 High potential variables.

Table 6.5 Analysis of various classifiers.

Table 6.6 Comparative analysis of various classifiers with their precision rates...

Chapter 7

Table 7.1 Number of tickets raised by 24 accounts/day and closed tickets/day: da...

Table 7.2 Overfitness, testing error and accuracy of the random forest model.

Chapter 8

Table 8.1 Buyer counts.

Table 8.2 Analysis of maximum likelihood estimates.

Chapter 11

Table 11.1 Shows cash flow statement source field in SAP System.

Table 11.2 Shows key figures, dimensions, and characteristics of infocube.

Chapter 13

Table 13.1 Summarized detail of source and target datasets.

Chapter 15

Table 15.1 Different waveforms present in the brain.

Table 15.2 CHB-MIT patient wise description.

Table 15.3 Ictal (Seizure), Inter-ictal (Normal), and Pre-ictal (Partial Seizure...

Table 15.4 Performance evaluation measures.

Table 15.5 Performance metric when learning rate is set to 0.001.

Table 15.6 Performance metric when learning rate is set to 0.01.

Table 15.7 Performance metrics when learning rate is set to 0.1.

Table 15.8 Comparative analysis of already proposed methodologies.

Chapter 16

Table 16.1 Labels for every forecast.

Table 16.2 Predictions mean various percentiles limits.

Guide

Cover

Table of Contents

Title Page

Copyright

Begin Reading

Index

End User License Agreement

Pages

v

ii

iii

iv

xvii

xviii

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

195

196

197

198

199

200

201

202

203

204

205

206

207

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

461

462

463

464

465

466

Scrivener Publishing100 Cummings Center, Suite 541JBeverly, 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 review of the state-of-the-art in data mining and machine learning,

A review and description of the learning methods in human-computer interaction,

Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time,

The scope and implementation of a majority of data mining and machine learning strategies, and

A discussion of real-time problems.

This book is a better choice than most other books available on the market because they were published a long time ago, and hence seldom elaborate on the current needs of data mining and machine learning. It is our hope that this book will promote mutual understanding among researchers in different disciplines, and facilitate future research development and collaborations.

We want to express our appreciation to all of the contributing authors who helped us tremendously with their contributions, time, critical thoughts, and suggestions to put together this peer-reviewed edited volume. The editors are also thankful to Scrivener Publishing and its team members for the opportunity to publish this volume. Lastly, we thank our family members for their love, support, encouragement, and patience during the entire period of this work.

Rohit RajaKapil Kumar NagwanshiSandeep KumarK. Ramya LaxmiNovember 2021

2Classification and Mining Behavior of Data

Srinivas Konda1*, Kavitarani Balmuri1 and Kishore Kumar Mamidala2

1Department of Computer Science and Engineering, CMR Technical Campus, Kandlakoya, Hyderabad, India

2Department of Computer Science and Engineering, Vivekananda Institute of Technology and Science, Karimnagar, India

Abstract

Behavior information is Information created by, or because of, a client’s commitment to a business. This can incorporate things like site visits, e-mail recruits, or other significant client activities. Regular wellsprings of conduct information incorporate sites, versatile applications, CRM frameworks, promoting computerization frameworks, call focuses, help work areas, and charging frameworks. Clients can either be purchasers, organizations, or people inside a business. However, conduct information can generally be tied back to a solitary end-client. Note that this client can be a known individual (signed in) or unknown (not signed in). Complex practices are broadly observed in fake and characteristic insightful frameworks, on the web, social and online systems, multi-operator frameworks, and mental frameworks. The inside and out comprehension of complex practices has been progressively perceived as a pivotal method for uncovering inside main impetuses, causes, and effects on organizations in taking care of many testing issues. Notwithstanding, customary conduct demonstrating primarily depends on subjective techniques from conduct science and sociology points of view. The purported conduct examination in information investigation and adapting regularly centers around human segment and business use Information, in which conduct situated components are covered up in regularly gathered value-based Information. Subsequently, it is inadequate or even difficult to profoundly investigate local conduct expectations, lifecycles, elements, and effects on complex issues and business issues.

Keywords: Data mining, knowledge discovery, web indexes, complex datasets, high-dimensional information, data organizations, data filtering, fleeting information

2.1 Introduction

In simple words, data mining is defined as a process often used to replace valuable data from a broad array of raw data. It suggests metadata design ideas in enormous data groupings using at least one computing. Data mining applies in different fields related to scientific facts and assessment. With mining techniques, organizations could even familiarize themselves with their customers and develop more successful processes recognized with different market capacities, thus influencing assets in a more ideal and adroit way. This makes organizations closer to their goal and better choices. Data mining techniques contain feasible information assortment and storage almost as Console preparation. To deform data and predict the risks of future occasions, information mining uses advanced quantitative measurements. Data mining is also known as Knowledge Discovery in Data (KDD).

With huge Information right now accessible and being gathered, acquiring admittance to Information is only occasionally the worry. Data is being created and put away at an exceptional rate, and progressively, a significant part of the large Information being gathered is about human conduct. This kind of Information is ordinarily made and put away as an “occasion,” which means a move that was made, with “properties,” which means meta-data used to depict the occasion. For instance, an occasion could be “site visit,” and property for that occasion could be “gadget type.” It might assist with considering occasions the “what” and the properties as the “who, when, and where.”

Our conduct is caught in the Data that we give from utilizing web indexes, e-business stages, informal community administrations, or online training. Filtering through this Information and determining bits of knowledge on human conduct empowers the stages to settle on more viable choices and offer better support. Nonetheless, customary conduct demonstrating depends on subjective strategies from conduct science and sociology viewpoints. There is an incredible requirement for computational models for assignments, for example, design examination, forecast, proposal, and abnormality recognition, on enormous scope datasets.

The information economy requires information mining to be more objective situated so more substantial outcomes can be created. This necessity infers that the semantics of the Information ought to be consolidated into the mining cycle. Information mining is prepared to manage this test since ongoing advancements in information mining have demonstrated an expanding enthusiasm for mining complex Information (as exemplified by chart mining, text mining, and so on). By consolidating the connections of the Information alongside the Information itself (instead of zeroing in on the Information alone), complex Information infuses semantics into the mining cycle, subsequently improving the capability of improving commitment to an information economy. Since the connections between the Information uncover certain social viewpoints hidden in the plain Information, this move of mining from straightforward Information to complex Information flags a key change to another phase in the exploration and practice of information disclosure, which can be named conduct mining. Conduct mining likewise has the capability of binding together some other ongoing exercises in information mining. We talk about significant viewpoints on conduct mining and examine its suggestions for the eventual fate of information mining.

This examination subject reports creative answers for issues of client conduct information scale in a wide scope of uses, for example, recommender frameworks and dubious conduct discovery. It covers information science and measurable ways to deal with information disclosure and demonstrating, choice help, and forecast, including AI and AI, on client conduct information. Potential settings incorporate Mining dynamic/streaming information, Mining diagram and system Information, Mining heterogeneous/multi-source information, Mining high dimensional information, Mining imbalanced information, Mining media information, Mining logical information, Mining successive information, Mining interpersonal organizations Mining spatial and transient Information.

2.2 Main Characteristics of Mining Behavioral Data

2.2.1 Mining Dynamic/Streaming Data

An information stream is a succession of unbounded, constant information things with an extremely high information rate that can just peruse once by an application [1, 2]. Information stream investigation has, as of late, stood out in the exploration network. Calculations for mining information streams and progressing ventures in business and logical applications have been created and talked about in [3, 4]. The vast majority of these calculations center around creating estimated one-pass strategies is shown in Figure 2.1.

Figure 2.1 Process of mining data stream.

Two ongoing progressions propel the requirement for information stream handling frameworks [5, 6]:

I. The programmed age of an exceptionally nitty gritty, high information rate succession of information things in various logical and business applications. For instance: satellite, radar, and cosmic information streams for logical applications and securities exchange and exchange web log information streams for business applications.

II. The requirement for complex investigations of these rapid information streams, for example, grouping and exception location, arrangement, regular item sets, and checking continuous things.

There are two techniques for tending to the issue of the fast idea of information streams. Information and yield rate variation of the mining calculation is the primary procedure. The rate transformation implies controlling the information and yield pace of the mining calculation as indicated by the accessible assets. The calculation estimate by growing new light-weight strategies that have just one glance at every information thing is the subsequent system. The principal focal point of mining information stream methods proposed so far is the structure of surmised mining calculations that have just one disregard or less the information stream [7].

2.2.2 Mining Graph & Network Data