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Data Mining and Machine Learning Applications
by Rohit Raja, Kapil Kumar Nagwanshi, Sandeep Kumar, K. Ramya Laxmi
Data Mining and Machine Learning Applications
Cover
Title Page
Copyright
Preface
1 Introduction to Data Mining
2 Classification and Mining Behavior of Data
3 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects
4 Stream Mining: Introduction, Tools & Techniques and Applications
5 Data Mining Tools and Techniques: Clustering Analysis
6 Data Mining Implementation Process
7 Predictive Analytics in IT Service Management (ITSM)
8 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques
9 Inductive Learning Including Decision Tree and Rule Induction Learning
10 Data Mining for Cyber-Physical Systems
11 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining
12 Human–Machine Interaction and Visual Data Mining
13 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection
14 New Algorithms and Technologies for Data Mining
15 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier
16 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques
17 Conclusion and Future Direction in Data Mining and Machine Learning
Index
End User License Agreement
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Data Mining and Machine Learning Applications
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
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