About this course: Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
Created by: University of Illinois at Urbana-Champaign
Taught by: Jiawei Han, Abel Bliss Professor
Department of Computer Science
| Basic Info |
Course 5 of 6 in the
Data Mining Specialization.
|
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Course Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
1 video, 3 readings, 1 practice quiz
- Video: Course Introduction
- Reading: Syllabus
- Reading: About the Discussion Forums
- Discussion Prompt: Getting to Know Your Classmates
- Reading: Social Media
- Practice Quiz: Orientation Quiz
Module 1
13 videos, 2 readings
- Reading: Lesson 1 Overview
- Video: 1.1. What is Cluster Analysis
- Video: 1.2. Applications of Cluster Analysis
- Video: 1.3 Requirements and Challenges
- Video: 1.4 A Multi-Dimensional Categorization
- Video: 1.5 An Overview of Typical Clustering Methodologies
- Video: 1.6 An Overview of Clustering Different Types of Data
- Video: 1.7 An Overview of User Insights and Clustering
- Reading: Lesson 2 Overview
- Video: 2.1 Basic Concepts: Measuring Similarity between Objects
- Video: 2.2 Distance on Numeric Data Minkowski Distance
- Video: 2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables
- Video: 2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types
- Video: 2.5 Proximity Measure between Two Vectors Cosine Similarity
- Video: 2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient
Graded: Lesson 1 Quiz
Graded: Lesson 2 Quiz
WEEK 2
Week 2
15 videos, 3 readings
- Reading: Lesson 3 Overview
- Video: 3.1 Partitioning-Based Clustering Methods
- Video: 3.2 K-Means Clustering Method
- Video: 3.3 Initialization of K-Means Clustering
- Video: 3.4 The K-Medoids Clustering Method
- Video: 3.5 The K-Medians and K-Modes Clustering Methods
- Video: 3.6 Kernel K-Means Clustering
- Reading: Lesson 4 Part 1 Overview
- Video: 4.1 Hierarchical Clustering Methods
- Video: 4.2 Agglomerative Clustering Algorithms
- Video: 4.3 Divisive Clustering Algorithms
- Video: 4.4 Extensions to Hierarchical Clustering
- Video: 4.5 BIRCH: A Micro-Clustering-Based Approach
- Reading: ClusterEnG Introduction
- Video: ClusterEnG Overview
- Video: ClusterEnG: K-Means and K-Medoids
- Video: ClusterEnG Application: AGNES
- Video: ClusterEnG Application: DBSCAN
Graded: Lesson 3 Quiz
Graded: Implementing the K-means Clustering Algorithm
WEEK 3
Week 3
9 videos, 2 readings
- Reading: Lesson 4 Part 2 Overview
- Video: 4.6 CURE: Clustering Using Well-Scattered Representatives
- Video: 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
- Video: 4.8 Probabilistic Hierarchical Clustering
- Reading: Lesson 5 Overview
- Video: 5.1 Density-Based and Grid-Based Clustering Methods
- Video: 5.2 DBSCAN: A Density-Based Clustering Algorithm
- Video: 5.3 OPTICS: Ordering Points To Identify Clustering Structure
- Video: 5.4 Grid-Based Clustering Methods
- Video: 5.5 STING: A Statistical Information Grid Approach
- Video: 5.6 CLIQUE: Grid-Based Subspace Clustering
Graded: Lesson 4 Quiz
Graded: Lesson 5 Quiz
WEEK 4
Week 4
10 videos, 1 reading
- Reading: Lesson 6 Overview
- Video: 6.1 Methods for Clustering Validation
- Video: 6.2 Clustering Evaluation Measuring Clustering Quality
- Video: 6.3 Constraint-Based Clustering
- Video: 6.4 External Measures 1: Matching-Based Measures
- Video: 6.5 External Measure 2: Entropy-Based Measures
- Video: 6.6 External Measure 3: Pairwise Measures
- Video: 6.7 Internal Measures for Clustering Validation
- Video: 6.8 Relative Measures
- Video: 6.9 Cluster Stability
- Video: 6.10 Clustering Tendency
Graded: Lesson 6 Quiz
Graded: Implementing Clustering Validation Measures
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
1 item
- Discussion Prompt: Final Reflections
FAQs
How It Works
Coursework
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Help from Your Peers
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Certificates
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Creators
University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
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This course along with the Reading material proposed will give you a big picture of how clustering algorithms work, as well as clustering validation methodologies. It is really useful if you are thinking about applying such algorithms and understanding the state-of-the-art.