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Data Mining and Machine Learning Applications
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Страница 1
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
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1
Introduction to Data Mining
1.1 Introduction 1.1.1. Data Mining
1.2 Knowledge Discovery in Database (KDD)
1.2.1 Importance of Data Mining
1.2.2 Applications of Data Mining
1.2.3 Databases
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
1.7.1 Python for Data Mining
1.7.2 KNIME
1.7.3 Rapid Miner
References
Страница 27
2
Classification and Mining Behavior of Data
2.1 Introduction
2.2 Main Characteristics of Mining Behavioral Data 2.2.1 Mining Dynamic/Streaming Data
2.2.2 Mining Graph & Network Data
2.2.3 Mining Heterogeneous/Multi-Source Information
2.2.3.1 Multi-Source and Multidimensional Information
2.2.3.2 Multi-Relational Data
2.2.3.3 Background and Connected Data
2.2.3.4 Complex Data, Sequences, and Events
2.2.3.5 Data Protection and Morals
2.2.4 Mining High Dimensional Data
2.2.5 Mining Imbalanced Data
2.2.5.1 The Class Imbalance Issue
2.2.6 Mining Multimedia Data
2.2.6.1 Common Applications Multimedia Data Mining
2.2.6.2 Multimedia Data Mining Utilizations
2.2.6.2.1 Customer Insight
2.2.6.2.2 Reconnaissance
2.2.6.2.3 Versatility Prediction for Delay Reduction in WLAN Utilizing Location Tracking and Data Mining
2.2.6.3 Multimedia Database Management
2.2.7 Mining Scientific Data
2.2.8 Mining Sequential Data
2.2.9 Mining Social Networks
2.2.9.1 Social-Media Data Mining Reasons
2.2.10 Mining Spatial and Temporal Data
2.2.10.1 Utilizations of Spatial and Temporal Data Mining
2.3 Research Method
2.4 Results
2.5 Discussion
2.6 Conclusion
References
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