À propos de ce cours : 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.
Créé par : University of Illinois at Urbana-Champaign
Enseigné par : Jiawei Han, Abel Bliss Professor
Department of Computer Science
| Informations de base | Cours 5 de 6 dans Data Mining Specialization |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Terminez ce cours pour travailler en vue de :
Spécialisation Data Mining
Ce cours fait partie de la Spécialisation Data Mining de 6 cours proposée par University of Illinois at Urbana-Champaign
Master of Computer Science in Data Science
Ce cours fait partie du programme de 32 credit hours (8 courses) diplômes intégralement en ligne
Programme de cours
SEMAINE 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 vidéo, 3 lectures, 1 quiz pour s'exercer
- Reading: Syllabus
- Reading: About the Discussion Forums
- Discussion Prompt: Getting to Know Your Classmates
- Reading: Social Media
- Practice Quiz: Orientation Quiz
Module 1
13 vidéos, 2 lectures
- Reading: Lesson 1 Overview
- Vidéo: 1.2. Applications of Cluster Analysis
- Vidéo: 1.3 Requirements and Challenges
- Vidéo: 1.4 A Multi-Dimensional Categorization
- Vidéo: 1.5 An Overview of Typical Clustering Methodologies
- Vidéo: 1.6 An Overview of Clustering Different Types of Data
- Vidéo: 1.7 An Overview of User Insights and Clustering
- Reading: Lesson 2 Overview
- Vidéo: 2.1 Basic Concepts: Measuring Similarity between Objects
- Vidéo: 2.2 Distance on Numeric Data Minkowski Distance
- Vidéo: 2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables
- Vidéo: 2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types
- Vidéo: 2.5 Proximity Measure between Two Vectors Cosine Similarity
- Vidéo: 2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient
Noté: Lesson 1 Quiz
Noté: Lesson 2 Quiz
SEMAINE 2
Week 2
15 vidéos, 3 lectures
- Reading: Lesson 3 Overview
- Vidéo: 3.2 K-Means Clustering Method
- Vidéo: 3.3 Initialization of K-Means Clustering
- Vidéo: 3.4 The K-Medoids Clustering Method
- Vidéo: 3.5 The K-Medians and K-Modes Clustering Methods
- Vidéo: 3.6 Kernel K-Means Clustering
- Reading: Lesson 4 Part 1 Overview
- Vidéo: 4.1 Hierarchical Clustering Methods
- Vidéo: 4.2 Agglomerative Clustering Algorithms
- Vidéo: 4.3 Divisive Clustering Algorithms
- Vidéo: 4.4 Extensions to Hierarchical Clustering
- Vidéo: 4.5 BIRCH: A Micro-Clustering-Based Approach
- Reading: ClusterEnG Introduction
- Vidéo: ClusterEnG Overview
- Vidéo: ClusterEnG: K-Means and K-Medoids
- Vidéo: ClusterEnG Application: AGNES
- Vidéo: ClusterEnG Application: DBSCAN
Noté: Lesson 3 Quiz
Noté: Implementing the K-means Clustering Algorithm
SEMAINE 3
Week 3
9 vidéos, 2 lectures
- Reading: Lesson 4 Part 2 Overview
- Vidéo: 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
- Vidéo: 4.8 Probabilistic Hierarchical Clustering
- Reading: Lesson 5 Overview
- Vidéo: 5.1 Density-Based and Grid-Based Clustering Methods
- Vidéo: 5.2 DBSCAN: A Density-Based Clustering Algorithm
- Vidéo: 5.3 OPTICS: Ordering Points To Identify Clustering Structure
- Vidéo: 5.4 Grid-Based Clustering Methods
- Vidéo: 5.5 STING: A Statistical Information Grid Approach
- Vidéo: 5.6 CLIQUE: Grid-Based Subspace Clustering
Noté: Lesson 4 Quiz
Noté: Lesson 5 Quiz
SEMAINE 4
Week 4
10 vidéos, 1 lecture
- Reading: Lesson 6 Overview
- Vidéo: 6.2 Clustering Evaluation Measuring Clustering Quality
- Vidéo: 6.3 Constraint-Based Clustering
- Vidéo: 6.4 External Measures 1: Matching-Based Measures
- Vidéo: 6.5 External Measure 2: Entropy-Based Measures
- Vidéo: 6.6 External Measure 3: Pairwise Measures
- Vidéo: 6.7 Internal Measures for Clustering Validation
- Vidéo: 6.8 Relative Measures
- Vidéo: 6.9 Cluster Stability
- Vidéo: 6.10 Clustering Tendency
Noté: Lesson 6 Quiz
Noté: Implementing Clustering Validation Measures
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
1 élément
- Discussion Prompt: Final Reflections
FAQ
Comment cela fonctionne
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
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Certificates
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
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.