Data Mining and Data Warehousing

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1. Introduction

What is Data Mining? Definition, Benefits
Classification of Data Mining Systems
Major Issues and Challenges in Data Mining
Data Mining Tools and Techniques
What are the Applications of Data Mining?

2. Data Warehousing

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3. Data Processing and Data Mining

What are the Data Mining Techniques
Terminologies used in association analysis

4. Classification and Prediction

Decision Tree classifier
Hunt’s Algorithm
ID3 Algorithm
Tree Pruning
Rule-Based Classifier
Instance Based Classifier
Bayesian Classifier
Artificial Neural Network (ANN) Classifier
Back Propagation Algorithm

5. Cluster Analysis

Cluster Analysis in Data Mining
Hierarchical agglomerative methods
K-means Clustering
Hierarchical Clustering
DBSCAN Clustering
Association Analysis in Data Mining
Association Rules Mining
Frequent Pattern (FP) Growth Method

6. Mining Complex Data Types

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What’s included?

  • 6 Chapters
  • 25 Article
  • 66 Questions
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