Intelligent Data Mining Techniques and Applications 1st Edition by Da Ruan, Guoqing Chen, Etienne E Kerre, Geert Wets ISBN 3642065767 9783642065767

Intelligent Data Mining Techniques and Applications 1st Edition by Da Ruan, Guoqing Chen, Etienne E Kerre, Geert Wets – Ebook PDF Instant Download/Delivery. 3642065767, 9783642065767
Full download Intelligent Data Mining Techniques and Applications 1st Edition after payment

Product details:

ISBN 10: 3642065767 
ISBN 13: 9783642065767
Author: Da Ruan, Guoqing Chen, Etienne E Kerre, Geert Wets

“Intelligent Data Mining – Techniques and Applications” is an organized edited collection of contributed chapters covering basic knowledge for intelligent systems and data mining, applications in economic and management, industrial engineering and other related industrial applications. The main objective of this book is to gather a number of peer-reviewed high quality contributions in the relevant topic areas. The focus is especially on those chapters that provide theoretical/analytical solutions to the problems of real interest in intelligent techniques possibly combined with other traditional tools, for data mining and the corresponding applications to engineers and managers of different industrial sectors. Academic and applied researchers and research students working on data mining can also directly benefit from this book.

Intelligent Data Mining Techniques and Applications 1st Table of contents:

• Introduction to Intelligent Data Mining

  • 1.1 What is Data Mining?
  • 1.2 The Role of Intelligence in Data Mining
  • 1.3 Importance of Data Mining in Today’s World
  • 1.4 Overview of Data Mining Techniques and Applications

• Fundamentals of Data Mining

  • 2.1 Data Mining Process: From Data Collection to Knowledge Discovery
  • 2.2 Types of Data: Structured, Unstructured, and Semi-structured
  • 2.3 Data Preprocessing: Cleaning, Transformation, and Reduction
  • 2.4 Tools and Techniques for Data Mining: Software and Algorithms

• Intelligent Data Mining Techniques

  • 3.1 Classification: Decision Trees, Random Forest, and Naive Bayes
  • 3.2 Clustering: K-Means, DBSCAN, and Hierarchical Clustering
  • 3.3 Association Rule Mining: Apriori and FP-Growth Algorithms
  • 3.4 Regression Analysis: Linear and Logistic Regression
  • 3.5 Anomaly Detection and Outlier Analysis

• Machine Learning Algorithms in Data Mining

  • 4.1 Supervised Learning vs. Unsupervised Learning
  • 4.2 Neural Networks and Deep Learning in Data Mining
  • 4.3 Support Vector Machines (SVM) and its Applications
  • 4.4 Reinforcement Learning in Data Mining
  • 4.5 Ensemble Methods: Bagging, Boosting, and Stacking

• Big Data and Data Mining

  • 5.1 Introduction to Big Data
  • 5.2 The Role of Data Mining in Big Data Analytics
  • 5.3 Tools for Big Data Mining: Hadoop, Spark, and NoSQL Databases
  • 5.4 Challenges and Opportunities in Big Data Mining

• Text Mining and Natural Language Processing (NLP)

  • 6.1 Basics of Text Mining and its Applications
  • 6.2 Sentiment Analysis and Opinion Mining
  • 6.3 Information Extraction and Text Classification
  • 6.4 NLP Techniques in Data Mining: Tokenization, Lemmatization, and Part-of-Speech Tagging

• Visual Data Mining

  • 7.1 Introduction to Visual Data Mining
  • 7.2 Data Visualization Techniques: Graphs, Charts, and Heatmaps
  • 7.3 Visualizing Large Datasets: Challenges and Best Practices
  • 7.4 Interactive Visualization Tools: Tableau, Power BI, and D3.js

• Applications of Intelligent Data Mining

  • 8.1 Healthcare: Predictive Analytics and Disease Diagnosis
  • 8.2 Finance: Fraud Detection and Risk Management
  • 8.3 Marketing: Customer Segmentation and Personalization
  • 8.4 Retail: Sales Forecasting and Recommendation Systems
  • 8.5 Social Media and Network Analysis

• Ethical and Privacy Concerns in Data Mining

  • 9.1 Data Privacy: Protecting Sensitive Information
  • 9.2 Ethical Implications of Data Mining and AI
  • 9.3 Bias and Fairness in Data Mining Models
  • 9.4 Legal and Regulatory Issues in Data Mining

• Advanced Topics in Intelligent Data Mining

  • 10.1 Deep Learning in Data Mining: Applications and Future Directions
  • 10.2 Data Mining for Internet of Things (IoT) and Smart Devices
  • 10.3 Privacy-Preserving Data Mining Techniques
  • 10.4 Knowledge Discovery in Complex and Dynamic Environments

• Tools and Platforms for Intelligent Data Mining

  • 11.1 Data Mining Software: Weka, RapidMiner, KNIME
  • 11.2 Programming for Data Mining: Python, R, and SQL
  • 11.3 Cloud-based Data Mining Platforms: AWS, Google Cloud, and Microsoft Azure
  • 11.4 Choosing the Right Tool for Your Data Mining Project

• Case Studies and Real-World Projects

  • 12.1 Case Study 1: Predicting Customer Churn in Telecom Industry
  • 12.2 Case Study 2: Analyzing Social Media Data for Trend Prediction
  • 12.3 Case Study 3: Healthcare Data Mining for Early Disease Detection
  • 12.4 Group Project: Developing a Data Mining Solution for a Real-World Problem

People also search for Intelligent Data Mining Techniques and Applications 1st:

intelligent data mining
    
vira intelligent data mining
    
vira intelligent data mining/persian llama-13b-instruct
    
intelligent data mining and fusion systems in agriculture
    
data mining and intelligent systems