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 视频, 3 阅读材料, 1 练习测试
- 视频: 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 视频, 2 阅读材料
- Reading: Lesson 1 Overview
- 视频: 1.1. What is Cluster Analysis
- 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
- Reading: 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 视频, 3 阅读材料
- Reading: Lesson 3 Overview
- 视频: 3.1 Partitioning-Based Clustering Methods
- 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
- Reading: 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
- Reading: 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 视频, 2 阅读材料
- Reading: Lesson 4 Part 2 Overview
- 视频: 4.6 CURE: Clustering Using Well-Scattered Representatives (Optional)
- 视频: 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
- 视频: 4.8 Probabilistic Hierarchical Clustering
- Reading: 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 视频, 1 阅读材料
- Reading: Lesson 6 Overview
- 视频: 6.1 Methods for Clustering Validation
- 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 项目
- Discussion Prompt: Final Reflections
常见问题解答
运作方式
Coursework
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Help from Your Peers
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Certificates
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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
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.
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.