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: Иллинойсский университет в Урбане-Шампейне
Преподаватели: Jiawei Han, Abel Bliss Professor
Department of Computer Science
| Основные сведения | Course 5 of 6 in the Data Mining Specialization |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
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 материалов для самостоятельного изучения, 1 practice quiz
- Видео: 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 видео, 2 материалов для самостоятельного изучения
- Reading: Lesson 1 Overview
- Видео: 1.1. What is Cluster Analysis
- 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 материалов для самостоятельного изучения
- Reading: Lesson 3 Overview
- Видео: 3.1 Partitioning-Based Clustering Methods
- 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 материалов для самостоятельного изучения
- Reading: Lesson 4 Part 2 Overview
- Видео: 4.6 CURE: Clustering Using Well-Scattered Representatives (Optional)
- 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: 6.1 Methods for Clustering Validation
- 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
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Авторы
Иллинойсский университет в Урбане-Шампейне
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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| Получить итоговую оценку | Доступен |
| Earn a shareable Course Certificate | Доступен |
Рейтинги и отзывы
Course is very good I learnt about a lot of things related to clustering. Actually it is a very good introductory course in clustering compared to the resources available online in general. Although few things that I think might help improve the course
i) Course only implements K-Means which is a very simple algorithm, instead of this or in addition to this implementation of few advanced algorithms like DBSCAN or CHAMELEON should be added.
ii) A no. of times prof only seems to be reading the slides which make things a little bit unclear i.e, the sentences used should be more common or explanatory rather than just reading the slides which the student itself can.
Apart from these things I truly enjoyed and learned many new things.
Thank you everyone involved in developing this course
Excellent overview of many clustering algorithms!
Great course that provides a good overview of different clustering approaches and how to deploy them to various problems. I found the lecture material unclear or vague at times, so that for certain topics understanding heavily depends on one diving through the provided reading material (which I found very helpful). However, the topic of evaluation is very dense in the lectures and the provided book chapters do not provide relevant insights as well, making the programming assignment for this part quite challenging (at least if not already deeply familiar already with the concepts involved). Be ready to invest effort to make the most of this.
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GL
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.