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
- リーディング: Syllabus
- リーディング: About the Discussion Forums
- ディスカッションのプロンプト: Getting to Know Your Classmates
- リーディング: Social Media
- 練習問題: Orientation Quiz
Module 1
13 个视频, 2 个阅读材料
- リーディング: 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
- リーディング: 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
- リーディング: 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
- リーディング: 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
- リーディング: 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
- リーディング: 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
- リーディング: 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
- リーディング: 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
- ディスカッションのプロンプト: Final Reflections
常见问题解答
运作方式
コースワーク
各コースは、インタラクティブな教科書のようなもので、事前に作成された動画、テスト、プロジェクトが備わっています。
他の受講生からの支援
他の数千もの受講生とつながり、アイデアやコース教材について検討し、 コンセプトの理解を深めます。
修了証
仕事に関する公式な認定を取得して、達成したことを友人、同僚、雇用主と共有してください。
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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