Table of Contents
Data Mining Techniques: Classification and Clustering
Today
What Is Classification?
Prediction, Clustering, Classification
Classification: 3 Step Process
Model Construction
Model Evaluation
Model Use: Classification
Classification Methods
Decision Trees
Decision Trees
Using Decision Trees for Classification
Decision Trees and Decision Rules
Top-Down Decision Tree Generation
Trees Construction Algorithm (ID3)
Choosing the “Best” Feature
Decision Tree Learning - Example
Decision Tree Learning - Example
Dealing With Continuous Variables
Improving on Information Gain
Over-fitting in Classification
Pruning the Decision Tree
Bayesian Classification
Naïve Bayesian Classifier
Naïve Bayesian Classifier - Example
Classification Example - Bank Data
Data Preparation
Data File Format for Weka
Data File Format for See5/C5
C4.5 Implementation in Weka
C4.5 Implementation in Weka
C4.5 Implementation in Weka
Classification Using See5/C5
Classification Using See5/C5
See5/C5: Applying Model to New Cases
Classification Using See5/C5
What is Clustering in Data Mining?
Requirements of Clustering Methods
Applications of Clustering
Clustering Methodologies
Distance or Similarity Measures
Distance or Similarity Measures
Distance or Similarity Measures
Distance or Similarity Measures
Distance (Similarity) Matrix
Example: Term Similarities in Documents
Similarity (Distance) Thresholds
Graph Representation
Simple Clustering Algorithms
Simple Clustering Algorithms
Simple Clustering Algorithms
Clustering with Existing Clusters
K-Means Algorithm
Example: K-Means
K-Means Algorithm
Hierarchical Algorithms
Hierarchical Agglomerative Clustering
Hierarchical Agglomerative Clustering
Clustering with CViz
Clustering with Cviz: The Data
Clustering with CViz
Clustering with CViz
Clustering with CViz
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