Об этом курсе: 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.
Автор: University of Illinois at Urbana-Champaign
Преподаватели: Jiawei Han, Abel Bliss Professor
Department of Computer Science
| Основные сведения | Курс 5 из 6 — Data Mining Specialization |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
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Специализация Data Mining
Этот курс входит в специализацию ''Data Mining ' из нескольких курсов (6) от партнера University of Illinois at Urbana-Champaign.
Master of Computer Science in Data Science
Этот курс входит в дипломную онлайн-программу из нескольких курсов (32 credit hours (8 courses)).
Программа курса
НЕДЕЛЯ 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 видео, 3 материала для самостоятельного изучения, 1 тренировочный тест
- Reading: Syllabus
- Reading: About the Discussion Forums
- Discussion Prompt: Getting to Know Your Classmates
- Reading: Social Media
- Practice Quiz: Orientation Quiz
Module 1
13 видео, 2 материала для самостоятельного изучения
- Reading: Lesson 1 Overview
- Видео: 1.2. Applications of Cluster Analysis
- Видео: 1.3 Requirements and Challenges
- Видео: 1.4 A Multi-Dimensional Categorization
- Видео: 1.5 An Overview of Typical Clustering Methodologies
- Видео: 1.6 An Overview of Clustering Different Types of Data
- Видео: 1.7 An Overview of User Insights and Clustering
- Reading: Lesson 2 Overview
- Видео: 2.1 Basic Concepts: Measuring Similarity between Objects
- Видео: 2.2 Distance on Numeric Data Minkowski Distance
- Видео: 2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables
- Видео: 2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types
- Видео: 2.5 Proximity Measure between Two Vectors Cosine Similarity
- Видео: 2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient
Оцениваемый: Lesson 1 Quiz
Оцениваемый: Lesson 2 Quiz
НЕДЕЛЯ 2
Week 2
15 видео, 3 материала для самостоятельного изучения
- Reading: Lesson 3 Overview
- Видео: 3.2 K-Means Clustering Method
- Видео: 3.3 Initialization of K-Means Clustering
- Видео: 3.4 The K-Medoids Clustering Method
- Видео: 3.5 The K-Medians and K-Modes Clustering Methods
- Видео: 3.6 Kernel K-Means Clustering
- Reading: Lesson 4 Part 1 Overview
- Видео: 4.1 Hierarchical Clustering Methods
- Видео: 4.2 Agglomerative Clustering Algorithms
- Видео: 4.3 Divisive Clustering Algorithms
- Видео: 4.4 Extensions to Hierarchical Clustering
- Видео: 4.5 BIRCH: A Micro-Clustering-Based Approach
- Reading: ClusterEnG Introduction
- Видео: ClusterEnG Overview
- Видео: ClusterEnG: K-Means and K-Medoids
- Видео: ClusterEnG Application: AGNES
- Видео: ClusterEnG Application: DBSCAN
Оцениваемый: Lesson 3 Quiz
Оцениваемый: Implementing the K-means Clustering Algorithm
НЕДЕЛЯ 3
Week 3
9 видео, 2 материала для самостоятельного изучения
- Reading: Lesson 4 Part 2 Overview
- Видео: 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
- Видео: 4.8 Probabilistic Hierarchical Clustering
- Reading: Lesson 5 Overview
- Видео: 5.1 Density-Based and Grid-Based Clustering Methods
- Видео: 5.2 DBSCAN: A Density-Based Clustering Algorithm
- Видео: 5.3 OPTICS: Ordering Points To Identify Clustering Structure
- Видео: 5.4 Grid-Based Clustering Methods
- Видео: 5.5 STING: A Statistical Information Grid Approach
- Видео: 5.6 CLIQUE: Grid-Based Subspace Clustering
Оцениваемый: Lesson 4 Quiz
Оцениваемый: Lesson 5 Quiz
НЕДЕЛЯ 4
Week 4
10 видео, 1 материал для самостоятельного изучения
- Reading: Lesson 6 Overview
- Видео: 6.2 Clustering Evaluation Measuring Clustering Quality
- Видео: 6.3 Constraint-Based Clustering
- Видео: 6.4 External Measures 1: Matching-Based Measures
- Видео: 6.5 External Measure 2: Entropy-Based Measures
- Видео: 6.6 External Measure 3: Pairwise Measures
- Видео: 6.7 Internal Measures for Clustering Validation
- Видео: 6.8 Relative Measures
- Видео: 6.9 Cluster Stability
- Видео: 6.10 Clustering Tendency
Оцениваемый: Lesson 6 Quiz
Оцениваемый: Implementing Clustering Validation Measures
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
1 элемент
- Discussion Prompt: Final Reflections
Часто задаваемые вопросы
Как это работает
Coursework
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Авторы
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.
Стоимость
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Рейтинги и отзывы
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.