关于此课程： 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.
制作方： 伊利诺伊大学香槟分校
教学方： Jiawei Han, Abel Bliss Professor
Department of Computer Science
| 基本信息 | 课程 5（共 6 门，Data Mining Specialization ） |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
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 个视频, 3 个阅读材料, 1 个练习测验
- 阅读: Syllabus
- 阅读: About the Discussion Forums
- 讨论提示: Getting to Know Your Classmates
- 阅读: Social Media
- 练习测验: Orientation Quiz
Module 1
13 个视频, 2 个阅读材料
- 阅读: Lesson 1 Overview
- 视频: 1.2. Applications of Cluster Analysis
- 视频: 1.3 Requirements and Challenges
- 视频: 1.4 A Multi-Dimensional Categorization
- 视频: 1.5 An Overview of Typical Clustering Methodologies
- 视频: 1.6 An Overview of Clustering Different Types of Data
- 视频: 1.7 An Overview of User Insights and Clustering
- 阅读: Lesson 2 Overview
- 视频: 2.1 Basic Concepts: Measuring Similarity between Objects
- 视频: 2.2 Distance on Numeric Data Minkowski Distance
- 视频: 2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables
- 视频: 2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types
- 视频: 2.5 Proximity Measure between Two Vectors Cosine Similarity
- 视频: 2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient
已评分: Lesson 1 Quiz
已评分: Lesson 2 Quiz
第 2 周
Week 2
15 个视频, 3 个阅读材料
- 阅读: Lesson 3 Overview
- 视频: 3.2 K-Means Clustering Method
- 视频: 3.3 Initialization of K-Means Clustering
- 视频: 3.4 The K-Medoids Clustering Method
- 视频: 3.5 The K-Medians and K-Modes Clustering Methods
- 视频: 3.6 Kernel K-Means Clustering
- 阅读: Lesson 4 Part 1 Overview
- 视频: 4.1 Hierarchical Clustering Methods
- 视频: 4.2 Agglomerative Clustering Algorithms
- 视频: 4.3 Divisive Clustering Algorithms
- 视频: 4.4 Extensions to Hierarchical Clustering
- 视频: 4.5 BIRCH: A Micro-Clustering-Based Approach
- 阅读: ClusterEnG Introduction
- 视频: ClusterEnG Overview
- 视频: ClusterEnG: K-Means and K-Medoids
- 视频: ClusterEnG Application: AGNES
- 视频: ClusterEnG Application: DBSCAN
已评分: Lesson 3 Quiz
已评分: Implementing the K-means Clustering Algorithm
第 3 周
Week 3
9 个视频, 2 个阅读材料
- 阅读: Lesson 4 Part 2 Overview
- 视频: 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
- 视频: 4.8 Probabilistic Hierarchical Clustering
- 阅读: Lesson 5 Overview
- 视频: 5.1 Density-Based and Grid-Based Clustering Methods
- 视频: 5.2 DBSCAN: A Density-Based Clustering Algorithm
- 视频: 5.3 OPTICS: Ordering Points To Identify Clustering Structure
- 视频: 5.4 Grid-Based Clustering Methods
- 视频: 5.5 STING: A Statistical Information Grid Approach
- 视频: 5.6 CLIQUE: Grid-Based Subspace Clustering
已评分: Lesson 4 Quiz
已评分: Lesson 5 Quiz
第 4 周
Week 4
10 个视频, 1 个阅读材料
- 阅读: Lesson 6 Overview
- 视频: 6.2 Clustering Evaluation Measuring Clustering Quality
- 视频: 6.3 Constraint-Based Clustering
- 视频: 6.4 External Measures 1: Matching-Based Measures
- 视频: 6.5 External Measure 2: Entropy-Based Measures
- 视频: 6.6 External Measure 3: Pairwise Measures
- 视频: 6.7 Internal Measures for Clustering Validation
- 视频: 6.8 Relative Measures
- 视频: 6.9 Cluster Stability
- 视频: 6.10 Clustering Tendency
已评分: Lesson 6 Quiz
已评分: Implementing Clustering Validation Measures
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
1 个项目
- 讨论提示: Final Reflections
常见问题解答
运作方式
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
来自同学的帮助
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
证书
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
制作方
伊利诺伊大学香槟分校
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.
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评分和审阅
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.
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
b
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.