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 |
Complete this course to work toward:
Data Mining Specialization
This course is part of the 6-course Data Mining Specialization from University of Illinois at Urbana-Champaign
Master of Computer Science in Data Science
This course is part of the fully-online 32 credit hours (8 courses) degree program
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
- Reading: Syllabus
- Reading: About the Discussion Forums
- Discussion Prompt: Getting to Know Your Classmates
- Reading: Social Media
- Cuestionario de práctica: Orientation Quiz
- Complemento no calificado: Welcome! Please tell us about yourself.
Module 1
13 videos, 2 readings
- Reading: Lesson 1 Overview
- 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: 3.2 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: 4.7 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: 6.2 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.
2 items
- Discussion Prompt: Final Reflections
- Complemento no calificado: How was the course?
FAQs
How It Works
Trabajo del curso
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Ayuda de tus compañeros
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Certificados
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
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
| Purchase Course | |
|---|---|
| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Ratings and Reviews
This course is a great resource to learn about the different clustering algorithms out there. I need to solve a clustering problem in my research and my knowledge about clustering ended at kmeans. The course teaches systematic ways to find out whether you should be clustering your data in the first place, what clustering algorithm should be best for your data, and how to evaluate the goodness of the algorithm and the used parameters. Many unknown unknowns have been illuminated to me by the course.
Gave a very good understanding of cluster analysis - explaining all different methods and algorithms, the benefits and drawbacks of each. The tool ClusterEng looks very good and can help in a lot of situations. Thank so much
This is a very good course covering all area of clustering. The only thing I feel a little struggle is some algorithm explained too brief, I prefer some detail step by step examples.
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IW
Nice lecture.
The programming assignment is difficult, more instructions could be provided.