Data Mining and Machine Learning Applications. Группа авторов

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      3 Chapter 3Figure 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.

      4 Chapter 4Figure 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.

      5 Chapter 5Figure 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...

      6 Chapter 6Figure 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.

      7 Chapter 7Figure 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...

      8 Chapter 8Figure 8.1 Process life cycle.Figure 8.2 Confusion matrices.

      9 Chapter 9Figure 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...

      10 Chapter 10Figure 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...

      11 Chapter 11Figure 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.

      12 Chapter 12Figure 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.

      13 Chapter 13Figure 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...

      14 Chapter 14Figure 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.

      15 Chapter 15Figure 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...

      16 Chapter 16Figure 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 СКАЧАТЬ