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E-raamat: Data Mining and Machine Learning Applications

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  • Ilmumisaeg: 27-Jan-2022
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119792512
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Data Mining and Machine Learning ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Jan-2022
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119792512
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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.

Preface xvii
1 Introduction to Data Mining
1(20)
Santosh R. Durugkar
Rohit Raja
Kapil Kumar Nagwanshi
Sandeep Kumar
1.1 Introduction
1(1)
1.1.1 Data Mining
1(1)
1.2 Knowledge Discovery in Database (KDD)
2(4)
1.2.1 Importance of Data Mining
3(1)
1.2.2 Applications of Data Mining
3(1)
1.2.3 Databases
4(2)
1.3 Issues in Data Mining
6(1)
1.4 Data Mining Algorithms
7(2)
1.5 Data Warehouse
9(1)
1.6 Data Mining Techniques
10(1)
1.7 Data Mining Tools
11(10)
1.7.1 Python for Data Mining
12(1)
1.7.2 KNIME
13(4)
1.7.3 Rapid Miner
17(1)
References
18(3)
2 Classification and Mining Behavior of Data
21(36)
Srinivas Konda
Kavitarani Balmuri
Kishore Kumar Mamidala
2.1 Introduction
22(1)
2.2 Main Characteristics of Mining Behavioral Data
23(21)
2.2.1 Mining Dynamic/Streaming Data
23(1)
2.2.2 Mining Graph & Network Data
24(1)
2.2.3 Mining Heterogeneous/Multi-Source Information
25(1)
2.2.3.1 Multi-Source and Multidimensional Information
26(1)
2.2.3.2 Multi-Relational Data
26(1)
2.2.3.3 Background and Connected Data
27(1)
2.2.3.4 Complex Data, Sequences, and Events
27(1)
2.2.3.5 Data Protection and Morals
27(1)
2.2.4 Mining High Dimensional Data
28(1)
2.2.5 Mining Imbalanced Data
29(1)
2.2.5.1 The Class Imbalance Issue
29(1)
2.2.6 Mining Multimedia Data
30(1)
2.2.6.1 Common Applications Multimedia Data Mining
31(1)
2.2.6.2 Multimedia Data Mining Utilizations
31(1)
2.2.6.3 Multimedia Database Management
32(2)
2.2.7 Mining Scientific Data
34(1)
2.2.8 Mining Sequential Data
35(1)
2.2.9 Mining Social Networks
36(3)
2.2.9.1 Social-Media Data Mining Reasons
39(1)
2.2.10 Mining Spatial and Temporal Data
40(1)
2.2.10.1 Utilizations of Spatial and Temporal Data Mining
41(3)
2.3 Research Method
44(4)
2.4 Results
48(1)
2.5 Discussion
49(1)
2.6 Conclusion
50(7)
References
51(6)
3 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects
57(42)
Rakhi Seth
Aakanksha Sharaff
3.1 Introduction
58(2)
3.2 Related Work on Different Recommender System
60(39)
3.2.1 Challenges in RS
65(1)
3.2.2 Research Questions and Architecture of This Paper
66(2)
3.2.3 Background
68(1)
3.2.3.1 The Architecture of Hybrid Approach
69(9)
3.2.4 Analysis
78(1)
3.2.4.1 Evaluation Measures
78(3)
3.2.5 Materials and Methods
81(4)
3.2.6 Comparative Analysis With Traditional Recommender System
85(1)
3.2.7 Practical Implications
85(9)
3.2.8 Conclusion & Future Work
94(1)
References
94(5)
4 Stream Mining: Introduction, Tools & Techniques and Applications
99(26)
Naresh Kumar Nagwani
4.1 Introduction
100(1)
4.2 Data Reduction: Sampling and Sketching
101(2)
4.2.1 Sampling
101(1)
4.2.2 Sketching
102(1)
4.3 Concept Drift
103(2)
4.4 Stream Mining Operations
105(4)
4.4.1 Clustering
105(1)
4.4.2 Classification
106(1)
4.4.3 Outlier Detection
107(1)
4.4.4 Frequent Itemsets Mining
108(1)
4.5 Tools & Techniques
109(11)
4.5.1 Implementation in Java
110(6)
4.5.2 Implementation in Python
116(2)
4.5.3 Implementation in R
118(2)
4.6 Applications
120(2)
4.6.1 Stock Prediction in Share Market
120(1)
4.6.2 Weather Forecasting System
121(1)
4.6.3 Finding Trending News and Events
121(1)
4.6.4 Analyzing User Behavior in Electronic Commerce Site (Click Stream)
121(1)
4.6.5 Pollution Control Systems
122(1)
4.7 Conclusion
122(3)
References
122(3)
5 Data Mining Tools and Techniques: Clustering Analysis
125(26)
Rohit Miri
Amit Kumar Dewangan
S.R. Tandan
Priya BhatnagarandHiral Raja
5.1 Introduction
126(3)
5.2 Data Mining Task
129(2)
5.2.1 Data Summarization
129(1)
5.2.2 Data Clustering
129(1)
5.2.3 Classification of Data
129(1)
5.2.4 Data Regression
130(1)
5.2.5 Data Association
130(1)
5.3 Data Mining Algorithms and Methodologies
131(5)
5.3.1 Data Classification Algorithm
131(1)
5.3.2 Predication
132(1)
5.3.3 Association Rule
132(1)
5.3.4 Neural Network
132(1)
5.3.4.1 Data Clustering Algorithm
133(1)
5.3.5 In-Depth Study of Gathering Techniques
134(1)
5.3.6 Data Partitioning Method
134(1)
5.3.7 Hierarchical Method
134(2)
5.3.8 Framework-Based Method
136(1)
5.3.9 Model-Based Method
136(1)
5.3.10 Thickness-Based Method
136(1)
5.4 Clustering the Nearest Neighbor
136(2)
5.4.1 Fuzzy Clustering
137(1)
5.4.2 K-Algorithm Means
137(1)
5.5 Data Mining Applications
138(2)
5.6 Materials and Strategies for Document Clustering
140(3)
5.6.1 Features Generation
142(1)
5.7 Discussion and Results
143(8)
5.7.1 Discussion
146(3)
5.7.2 Conclusion
149(1)
References
149(2)
6 Data Mining Implementation Process
151(24)
Kamal K. Mehta
Rajesh Tiwari
Nishant Behar
6.1 Introduction
151(1)
6.2 Data Mining Historical Trends
152(1)
6.3 Processes of Data Analysis
153(22)
6.3.1 Data Attack
153(1)
6.3.2 Data Mixing
153(1)
6.3.3 Data Collection
153(1)
6.3.4 Data Conversion
154(1)
6.3.4.1 Data Mining
154(1)
6.3.4.2 Design Evaluation
154(1)
6.3.4.3 Data Illustration
154(1)
6.3.4.4 Implementation of Data Mining in the Cross-Industry Standard Process
154(1)
6.3.5 Business Understanding
155(1)
6.3.6 Data Understanding
156(2)
6.3.7 Data Preparation
158(1)
6.3.8 Modeling
159(1)
6.3.9 Evaluation
160(1)
6.3.10 Deployment
161(1)
6.3.11 Contemporary Developments
162(1)
6.3.12 An Assortment of Data Mining
162(1)
6.3.12.1 Using Computational & Connectivity Tools
163(1)
6.3.12.2 Web Mining
163(1)
6.3.12.3 Comparative Statement
163(1)
6.3.13 Advantages of Data Mining
163(2)
6.3.14 Drawbacks of Data Mining
165(1)
6.3.15 Data Mining Applications
165(2)
6.3.16 Methodology
167(2)
6.3.17 Results
169(2)
6.3.18 Conclusion and Future Scope
171(1)
References
172(3)
7 Predictive Analytics in IT Service Management (ITSM)
175(20)
Sharon Christa I.L.
Suma V.
7.1 Introduction
176(2)
7.2 Analytics: An Overview
178(3)
7.2.1 Predictive Analytics
180(1)
7.3 Significance of Predictive Analytics in ITSM
181(5)
7.4 Ticket Analytics: A Case Study
186(5)
7.4.1 Input Parameters
188(1)
7.4.2 Predictive Modeling
188(1)
7.4.3 Random Forest Model
189(2)
7.4.4 Performance of the Predictive Model
191(1)
7.5 Conclusion
191(4)
References
192(3)
8 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques
195(14)
Ramya Laxmi Jr.
Sumit Srivastava
K. Madhuravani
S. Pallaviand Omprakash Dewangan
8.1 Introduction
196(2)
8.2 Literature Review
198(2)
8.3 Methodology and Implementation
200(3)
8.3.1 Selection of the Independent Variables
200(3)
8.4 Data Partitioning
203(1)
8.4.1 Interpreting the Results of Logistic Regression Model
203(1)
8.5 Conclusions
204(5)
References
205(4)
9 Inductive Learning Including Decision Tree and Rule Induction Learning
209(26)
Raj Kumar Patra
A. Mahendar
G. Madhukar
9.1 Introduction
210(2)
9.2 The Inductive Learning Algorithm (ILA)
212(1)
9.3 Proposed Algorithms
213(1)
9.4 Divide & Conquer Algorithm
214(1)
9.4.1 Decision Tree
214(1)
9.5 Decision Tree Algorithms
215(16)
9.5.1 ID3 Algorithm
215(2)
9.5.2 Separate and Conquer Algorithm
217(9)
9.5.3 RULE EXTRACTOR-1
226(1)
9.5.4 Inductive Learning Applications
226(1)
9.5.4.1 Education
226(1)
9.5.4.2 Making Credit Decisions
227(1)
9.5.5 Multidimensional Databases and OLAP
228(1)
9.5.6 Fuzzy Choice Trees
228(1)
9.5.7 Fuzzy Choice Tree Development From a Multidimensional Database
229(1)
9.5.8 Execution and Results
230(1)
9.6 Conclusion and Future Work
231(4)
References
232(3)
10 Data Mining for Cyber-Physical Systems
235(46)
M. Varaprasad Rao
D. Anji Reddy
Anusha Ampavathi
Shaik Munawar
10.1 Introduction
236(4)
10.1.1 Models of Cyber-Physical System
238(1)
10.1.2 Statistical Model-Based Methodologies
239(1)
10.1.3 Spatial-and-Transient Closeness-Based Methodologies
240(1)
10.2 Feature Recovering Methodologies
240(1)
10.3 CPS vs. IT Systems
241(1)
10.4 Collections, Sources, and Generations of Big Data for CPS
242(1)
10.4.1 Establishing Conscious Computation and Information Systems
243(1)
10.5 Spatial Prediction
243(5)
10.5.1 Global Optimization
244(1)
10.5.2 Big Data Analysis CPS
245(1)
10.5.3 Analysis of Cloud Data
245(2)
10.5.4 Analysis of Multi-Cloud Data
247(1)
10.6 Clustering of Big Data
248(3)
10.7 NoSQL
251(1)
10.8 Cyber Security and Privacy Big Data
251(5)
10.8.1 Protection of Big Computing and Storage
252(1)
10.8.2 Big Data Analytics Protection
252(4)
10.8.3 Big Data CPS Applications
256(1)
10.9 Smart Grids
256(2)
10.10 Military Applications
258(1)
10.11 City Management
259(2)
10.12 Clinical Applications
261(1)
10.13 Calamity Events
262(1)
10.14 Data Streams Clustering by Sensors
263(1)
10.15 The Flocking Model
263(1)
10.16 Calculation Depiction
264(1)
10.17 Initialization
265(1)
10.18 Representative Maintenance and Clustering
266(1)
10.19 Results
267(1)
10.20 Conclusion
268(13)
References
269(12)
11 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining
281(36)
Vivek Parganiha
Soorya Prakash Shukla
Lokesh Kumar Sharma
11.1 Introduction
282(1)
11.2 Background
283(1)
11.3 Methodology of CRISP-DM
284(2)
11.4 Stage One--Determine Business Objectives
286(4)
11.4.1 What Are the Ideal Yields of the Venture?
287(1)
11.4.2 Evaluate the Current Circumstance
288(1)
11.4.3 Realizes Data Mining Goals
289(1)
11.5 Stage Two--Data Sympathetic
290(2)
11.5.1 Portray Data
291(1)
11.5.2 Investigate Facts
291(1)
11.5.3 Confirm Data Quality
292(1)
11.5.4 Data Excellence Description
292(1)
11.6 Stage Three--Data Preparation
292(3)
11.6.1 Select Your Data
294(1)
11.6.2 The Data Is Processed
294(1)
11.6.3 Data Needed to Build
294(1)
11.6.4 Combine Information
295(1)
11.7 Stage Four--Modeling
295(3)
11.7.1 Select Displaying Strategy
296(1)
11.7.2 Produce an Investigation Plan
297(1)
11.7.3 Fabricate Ideal
297(1)
11.7.4 Evaluation Model
297(1)
11.8 Stage Five--Evaluation
298(2)
11.8.1 Assess Your Outcomes
299(1)
11.8.2 Survey Measure
299(1)
11.8.3 Decide on the Subsequent Stages
300(1)
11.9 Stage Six--Deployment
300(2)
11.9.1 Plan Arrangement
301(1)
11.9.2 Plan Observing and Support
301(1)
11.9.3 Produce the Last Report
302(1)
11.9.4 Audit Venture
302(1)
11.10 Data on ERP Systems
302(2)
11.11 Usage of CRISP-DM Methodology
304(2)
11.12 Modeling
306(4)
11.12.1 Association Rule Mining (ARM) or Association Analysis
307(1)
11.12.2 Classification Algorithms
307(1)
11.12.3 Regression Algorithms
308(1)
11.12.4 Clustering Algorithms
308(2)
11.13 Assessment
310(1)
11.14 Distribution
310(1)
11.15 Results and Discussion
310(1)
11.16 Conclusion
311(6)
References
314(3)
12 Human-Machine Interaction and Visual Data Mining
317(32)
Upasana Sinha
Akanksha Gupta
Santera Khan
Shilpa Rani
Swati Jain
12.1 Introduction
318(2)
12.2 Related Researches
320(5)
12.2.1 Data Mining
323(1)
12.2.2 Data Visualization
323(1)
12.2.3 Visual Learning
324(1)
12.3 Visual Genes
325(1)
12.4 Visual Hypotheses
326(1)
12.5 Visual Strength and Conditioning
326(1)
12.6 Visual Optimization
327(1)
12.7 The Vis 09 Model
327(1)
12.8 Graphic Monitoring and Contact With Human-Computer
328(4)
12.9 Mining HCI Information Using Inductive Deduction Viewpoint
332(2)
12.10 Visual Data Mining Methodology
334(4)
12.11 Machine Learning Algorithms for Hand Gesture Recognition
338(1)
12.12 Learning
338(1)
12.13 Detection
339(1)
12.14 Recognition
340(1)
12.15 Proposed Methodology for Hand Gesture Recognition
340(3)
12.16 Result
343(1)
12.17 Conclusion
343(6)
References
344(5)
13 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection
349(16)
Lokesh Singh
Rekh Ram Janghel
Satya Prakash Sahu
13.1 Introduction
349(3)
13.2 Literature Survey
352(1)
13.3 Methods and Material
353(4)
13.3.1 Proposed Methodology: Multi Source Dynamic TrAdaBoost Algorithm
355(2)
13.4 Experimental Results
357(1)
13.5 Libraries Used
357(1)
13.6 Comparing Algorithms Based on Decision Boundaries
357(1)
13.7 Evaluating Results
358(3)
13.8 Conclusion
361(4)
References
361(4)
14 New Algorithms and Technologies for Data Mining
365(32)
Padma Bonde
Latika Pinjarkar
Korhan Cengiz
Aditi Shukla
Maguluri Sudeep Joel
14.1 Introduction
366(2)
14.2 Machine Learning Algorithms
368(1)
14.3 Supervised Learning
368(1)
14.4 Unsupervised Learning
369(1)
14.5 Semi-Supervised Learning
369(2)
14.6 Regression Algorithms
371(1)
14.7 Case-Based Algorithms
371(1)
14.8 Regularization Algorithms
372(1)
14.9 Decision Tree Algorithms
372(1)
14.10 Bayesian Algorithms
373(1)
14.11 Clustering Algorithms
374(1)
14.12 Association Rule Learning Algorithms
375(1)
14.13 Artificial Neural Network Algorithms
375(1)
14.14 Deep Learning Algorithms
376(1)
14.15 Dimensionality Reduction Algorithms
377(1)
14.16 Ensemble Algorithms
377(1)
14.17 Other Machine Learning Algorithms
378(1)
14.18 Data Mining Assignments
378(3)
14.19 Data Mining Models
381(1)
14.20 Non-Parametric & Parametric Models
381(1)
14.21 Flexible vs. Restrictive Methods
382(1)
14.22 Unsupervised vs. Supervised Learning
382(2)
14.23 Data Mining Methods
384(3)
14.24 Proposed Algorithm
387(1)
14.24.1 Organization Formation Procedure
387(1)
14.25 The Regret of Learning Phase
388(4)
14.26 Conclusion
392(5)
References
392(5)
15 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier
397(26)
Sudesh Kumar
Rekh Ram Janghel
Satya Prakash Sahu
15.1 Introduction
398(2)
15.2 Related Work
400(1)
15.3 Material and Methods
401(9)
15.3.1 Dataset Description
401(2)
15.3.2 Proposed Methodology
403(1)
15.3.3 Normalization
404(1)
15.3.4 Preprocessing Using PCA
404(2)
15.3.5 Restricted Boltzmann Machine (RBM)
406(1)
15.3.6 Stochastic Binary Units (Bernoulli Variables)
407(1)
15.3.7 Training
408(1)
15.3.7.1 Gibbs Sampling
409(1)
15.3.7.2 Contrastive Divergence (CD)
409(1)
15.4 Experimental Framework
410(2)
15.5 Experimental Results and Discussion
412(2)
15.5.1 Performance Measurement Criteria
412(1)
15.5.2 Experimental Results
412(2)
15.6 Discussion
414(4)
15.7 Conclusion
418(5)
References
419(4)
16 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques
423(24)
Nanda R. Wagh
Sanjay R. Sutar
16.1 Introduction
424(1)
16.2 Related Work
424(3)
16.2.1 WoSApp
424(1)
16.2.2 Abhaya
425(1)
16.2.3 Women Empowerment
425(1)
16.2.4 Nirbhaya
425(1)
16.2.5 Glympse
426(1)
16.2.6 Fightback
426(1)
16.2.7 Versatile-Based
426(1)
16.2.8 RFID
426(1)
16.2.9 Self-Preservation Framework for Women With Area Following and SMS Alarming Through GSM Network
426(1)
16.2.10 Safe: A Women Security Framework
427(1)
16.2.11 Intelligent Safety System For Women Security
427(1)
16.2.12 A Mobile-Based Women Safety Application
427(1)
16.2.13 Self-Salvation--The Women's Security Module
427(1)
16.3 Issue and Solution
427(1)
16.3.1 Inspiration
427(1)
16.3.2 Issue Statement and Choice of Solution
428(1)
16.4 Selection of Data
428(2)
16.5 Pre-Preparation Data
430(6)
16.5.1 Simulation
431(1)
16.5.2 Assessment
431(3)
16.5.3 Forecast
434(2)
16.6 Application Development
436(1)
16.6.1 Methodology
436(1)
16.6.2 AI Model
437(1)
16.6.3 Innovations Used The Proposed Application Has Utilized After Technologies
437(1)
16.7 Use Case For The Application
437(6)
16.7.1 Application Icon
437(1)
16.7.2 Enlistment Form
438(1)
16.7.3 Login Form
439(1)
16.7.4 Misconduct Place Detector
439(1)
16.7.5 Help Button
440(3)
16.8 Conclusion
443(4)
References
443(4)
17 Conclusion and Future Direction in Data Mining and Machine Learning
447(10)
Santosh R. Durugkar
Rohit Raja
Kapil Kumar Nagwanshi
Ramakant Chandrakar
17.1 Introduction
448(3)
17.2 Machine Learning
451(6)
17.2.1 Neural Network
452(1)
17.2.2 Deep Learning
452(1)
17.2.3 Three Activities for Object Recognition
453(4)
17.3 Conclusion
457(1)
References 457(4)
Index 461
Rohit Raja, PhD is an associate professor in the IT Department, Guru Ghasidas Vishwavidyalaya, Bilaspur (CG), India. He has published more than 80 research papers in peer-reviewed journals as well as 9 patents.

Kapil Kumar Nagwanshi, PhD is an associate professor at Mukesh Patel School of Technology Management & Engineering, Shirpur Campus, SVKMs Narsee Monjee Institute of Management Studies Mumbai, India.

Sandeep Kumar, PhD is a professor in the Department of Electronics & Communication Engineering, Sreyas Institute of Engineering & Technology, Hyderabad, India. His area of research includes embedded systems, image processing, and biometrics. He has published more than 60 research papers in peer-reviewed journals as well as 6 patents.

K. Ramya Laxmi, PhD is an associate professor in the CSE Department at the Sreyas Institute of Engineering and Technology, Hyderabad. Her research interest covers the fields of data mining and image processing.
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