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Eberly College of Science
STAT 897D
Applied Data Mining and Statistical Learning
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Lesson 12: Cluster Analysis
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Introduction
Key Learning Goals for this Lesson
understand the k-means clustering algorithm and know how to program it.
understand the basic idea of agglomerative clustering and the various schemes of updating between-cluster distances.
Textbook reading: Section 10.3.
12.1 - K-Means
12.2 - The Algorithm
12.3 - Examples
12.4 - Initialization
12.5 - R Scripts (K-means clustering)
12.6 - Agglomerative Clustering
12.7 - Pseudo Code
12.8 - R Scripts (Agglomerative Clustering)
12.1 - K-Means ›
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Welcome to STAT 897D - Applied Data Mining and Statistical Learning
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Lessons
Lesson 1(a): Introduction to Data Mining
Lesson 1 (b): Exploratory Data Analysis (EDA)
Lesson 2: Statistical Learning and Model Selection
Lesson 3 : Linear Regression
Lesson 4: Variable Selection
Lesson 5: Regression Shrinkage Methods
Lesson 6: Principal Components Analysis
Lesson 7: Dimension Reduction Methods
Lesson 8: Modeling Non-linear Relationships
Lesson 9: Classification
Lesson 10: Support Vector Machines
Lesson 11: Tree-based Methods
Lesson 12: Cluster Analysis
12.1 - K-Means
12.2 - The Algorithm
12.3 - Examples
12.4 - Initialization
12.5 - R Scripts (K-means clustering)
12.6 - Agglomerative Clustering
12.7 - Pseudo Code
12.8 - R Scripts (Agglomerative Clustering)
Resources
Analysis of German Credit Data
Analysis of Wine Quality Data
Analysis of Classification Data
Final Project - Sample Work