Informações sobre o curso: 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.
Desenvolvido por: University of Illinois at Urbana-Champaign
Ministrado por: Jiawei Han, Abel Bliss Professor
Department of Computer Science
| Informações básicas | Curso 5 de 6 no Data Mining Specialization |
| Idioma | English |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
Programa
SEMANA 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
- Leyendo: Syllabus
- Leyendo: About the Discussion Forums
- Cuadro de aviso de la discusión: Getting to Know Your Classmates
- Leyendo: Social Media
- Cuestionario de práctica: Orientation Quiz
Module 1
13 videos, 2 readings
- Leyendo: Lesson 1 Overview
- Vídeo: 1.2. Applications of Cluster Analysis
- Vídeo: 1.3 Requirements and Challenges
- Vídeo: 1.4 A Multi-Dimensional Categorization
- Vídeo: 1.5 An Overview of Typical Clustering Methodologies
- Vídeo: 1.6 An Overview of Clustering Different Types of Data
- Vídeo: 1.7 An Overview of User Insights and Clustering
- Leyendo: Lesson 2 Overview
- Vídeo: 2.1 Basic Concepts: Measuring Similarity between Objects
- Vídeo: 2.2 Distance on Numeric Data Minkowski Distance
- Vídeo: 2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables
- Vídeo: 2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types
- Vídeo: 2.5 Proximity Measure between Two Vectors Cosine Similarity
- Vídeo: 2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient
Nota atribuída: Lesson 1 Quiz
Nota atribuída: Lesson 2 Quiz
SEMANA 2
Week 2
15 videos, 3 readings
- Leyendo: Lesson 3 Overview
- Vídeo: 3.2 K-Means Clustering Method
- Vídeo: 3.3 Initialization of K-Means Clustering
- Vídeo: 3.4 The K-Medoids Clustering Method
- Vídeo: 3.5 The K-Medians and K-Modes Clustering Methods
- Vídeo: 3.6 Kernel K-Means Clustering
- Leyendo: Lesson 4 Part 1 Overview
- Vídeo: 4.1 Hierarchical Clustering Methods
- Vídeo: 4.2 Agglomerative Clustering Algorithms
- Vídeo: 4.3 Divisive Clustering Algorithms
- Vídeo: 4.4 Extensions to Hierarchical Clustering
- Vídeo: 4.5 BIRCH: A Micro-Clustering-Based Approach
- Leyendo: ClusterEnG Introduction
- Vídeo: ClusterEnG Overview
- Vídeo: ClusterEnG: K-Means and K-Medoids
- Vídeo: ClusterEnG Application: AGNES
- Vídeo: ClusterEnG Application: DBSCAN
Nota atribuída: Lesson 3 Quiz
Nota atribuída: Implementing the K-means Clustering Algorithm
SEMANA 3
Week 3
9 videos, 2 readings
- Leyendo: Lesson 4 Part 2 Overview
- Vídeo: 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
- Vídeo: 4.8 Probabilistic Hierarchical Clustering
- Leyendo: Lesson 5 Overview
- Vídeo: 5.1 Density-Based and Grid-Based Clustering Methods
- Vídeo: 5.2 DBSCAN: A Density-Based Clustering Algorithm
- Vídeo: 5.3 OPTICS: Ordering Points To Identify Clustering Structure
- Vídeo: 5.4 Grid-Based Clustering Methods
- Vídeo: 5.5 STING: A Statistical Information Grid Approach
- Vídeo: 5.6 CLIQUE: Grid-Based Subspace Clustering
Nota atribuída: Lesson 4 Quiz
Nota atribuída: Lesson 5 Quiz
SEMANA 4
Week 4
10 videos, 1 reading
- Leyendo: Lesson 6 Overview
- Vídeo: 6.2 Clustering Evaluation Measuring Clustering Quality
- Vídeo: 6.3 Constraint-Based Clustering
- Vídeo: 6.4 External Measures 1: Matching-Based Measures
- Vídeo: 6.5 External Measure 2: Entropy-Based Measures
- Vídeo: 6.6 External Measure 3: Pairwise Measures
- Vídeo: 6.7 Internal Measures for Clustering Validation
- Vídeo: 6.8 Relative Measures
- Vídeo: 6.9 Cluster Stability
- Vídeo: 6.10 Clustering Tendency
Nota atribuída: Lesson 6 Quiz
Nota atribuída: Implementing Clustering Validation Measures
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
1 item
- Cuadro de aviso de la discusión: Final Reflections
Perguntas frequentes
Como funciona
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.
Desenvolvedores
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.
Custo
| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
Classificações e avaliações
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