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Data Warehousing and Data Mining-Introduction▶️Makaut CSE IT 6th sem #makaut #dwdm #data #mining #ai
In this video, we’ll walk you through:
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1. Introduction to Data Warehousing and Data Mining

1.1 Introduction to Data Warehousing
Definition and Importance of Data Warehousing
Characteristics of Data Warehouses
Data Warehouse Architecture
Data Marts and OLAP (Online Analytical Processing)
1.2 Introduction to Data Mining
Definition and Objectives
KDD (Knowledge Discovery in Databases) Process
Applications of Data Mining
1.3 Frequent Pattern Mining
Mining Frequent Patterns
Association Rules and Correlation
1.4 Sequential Pattern Mining
Concepts and Primitives
Scalable Methods

2. Classification and Prediction
2.1 Classification
Definition and Use Cases
Decision Tree Classifiers
Naive Bayes Classifier
SVM (Support Vector Machine) for Classification
2.2 Prediction
Definition and Differences from Classification
Linear and Non-linear Regression
Evaluation Metrics for Predictive Models
2.3 Cluster Analysis
Types of Data in Cluster Analysis
Partitioning Methods
Hierarchical Clustering
2.4 Transactional and Temporal Patterns
Transactional Patterns
Temporal-Based Frequent Patterns

3. Mining Time-Series Data
3.1 Introduction to Time-Series Data
Nature of Time-Series Data
Data Preprocessing for Time-Series
3.2 Periodicity Analysis
Techniques for Periodicity Detection
Applications of Periodicity Analysis
3.3 Trend Analysis
Identifying and Analyzing Trends
Forecasting Techniques
3.4 Similarity Search
Methods for Similarity Search in Time-Series
Distance Metrics

4. Mining Data Streams
4.1 Stream Data Processing
Characteristics of Data Streams
Frameworks and Systems for Stream Mining
4.2 Frequent Pattern Mining
Frequent Pattern Discovery in Streams
Algorithms for Stream Data
4.3 Sequential Pattern Mining
Techniques for Sequence Discovery in Streams
4.4 Classification of Dynamic Data Streams
Adapting Classification Models
Class Imbalance Problem in Streams
4.5 Graph Mining and Social Network Analysis
Introduction to Graph Mining
Mining Social Network Data
4.6 Other Applications
Modulation for Communication
Filtering and Feedback Control Systems

5. Web Mining
5.1 Overview of Web Mining
Web Content, Structure, and Usage Mining
5.2 Mining Web Page Layout Structure
Techniques for Web Layout Mining
Case Studies
5.3 Mining Web Link Structure
PageRank and HITS Algorithms
Link Analysis Applications
5.4 Mining Multimedia Data on the Web
Challenges and Techniques
Applications in Image and Video Mining
5.5 Automatic Classification of Web Documents
Techniques and Algorithms
Case Studies
5.6 Web Usage Mining
Clickstream Analysis
Personalization and Recommendation Systems
5.7 Distributed Data Mining

6. Recent Trends in Distributed Warehousing and Data Mining
6.1 Distributed Data Warehousing
Definition and Architectures
Challenges and Future Trends
6.2 Class Imbalance Problem
Techniques for Handling Imbalance
6.3 Graph Mining
Advanced Algorithms for Graph Data
6.4 Social Network Analysis
Advanced Tools and Methods
6.5 Emerging Trends
AI and Machine Learning in Data Mining
Cloud-Based Data Warehousing


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