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.
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 1 小时
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 （总计 7 分钟）, 3 个阅读材料, 1 个测验
1 个视频
3 个阅读材料
Syllabus10分钟
About the Discussion Forums10分钟
Social Media10分钟
1 个练习
Orientation Quiz10分钟
完成时间为 2 小时
Module 1
13 个视频 （总计 65 分钟）, 2 个阅读材料, 2 个测验
13 个视频
1.2. Applications of Cluster Analysis2分钟
1.3 Requirements and Challenges5分钟
1.4 A Multi-Dimensional Categorization2分钟
1.5 An Overview of Typical Clustering Methodologies6分钟
1.6 An Overview of Clustering Different Types of Data6分钟
1.7 An Overview of User Insights and Clustering3分钟
2.1 Basic Concepts: Measuring Similarity between Objects3分钟
2.2 Distance on Numeric Data Minkowski Distance7分钟
2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables4分钟
2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types4分钟
2.5 Proximity Measure between Two Vectors Cosine Similarity2分钟
2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient13分钟
2 个阅读材料
Lesson 1 Overview10分钟
Lesson 2 Overview10分钟
2 个练习
Lesson 1 Quiz8分钟
Lesson 2 Quiz12分钟
周
2完成时间为 5 小时
Week 2
15 个视频 （总计 78 分钟）, 3 个阅读材料, 2 个测验
15 个视频
3.2 K-Means Clustering Method9分钟
3.3 Initialization of K-Means Clustering4分钟
3.4 The K-Medoids Clustering Method6分钟
3.5 The K-Medians and K-Modes Clustering Methods6分钟
3.6 Kernel K-Means Clustering8分钟
4.1 Hierarchical Clustering Methods1分钟
4.2 Agglomerative Clustering Algorithms8分钟
4.3 Divisive Clustering Algorithms3分钟
4.4 Extensions to Hierarchical Clustering3分钟
4.5 BIRCH: A Micro-Clustering-Based Approach7分钟
ClusterEnG Overview5分钟
ClusterEnG: K-Means and K-Medoids3分钟
ClusterEnG Application: AGNES4分钟
ClusterEnG Application: DBSCAN2分钟
3 个阅读材料
Lesson 3 Overview10分钟
Lesson 4 Part 1 Overview10分钟
ClusterEnG Introduction10分钟
1 个练习
Lesson 3 Quiz10分钟
周
3完成时间为 1 小时
Week 3
9 个视频 （总计 53 分钟）, 2 个阅读材料, 2 个测验
9 个视频
4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data8分钟
4.8 Probabilistic Hierarchical Clustering7分钟
5.1 Density-Based and Grid-Based Clustering Methods1分钟
5.2 DBSCAN: A Density-Based Clustering Algorithm8分钟
5.3 OPTICS: Ordering Points To Identify Clustering Structure9分钟
5.4 Grid-Based Clustering Methods3分钟
5.5 STING: A Statistical Information Grid Approach3分钟
5.6 CLIQUE: Grid-Based Subspace Clustering7分钟
2 个阅读材料
Lesson 4 Part 2 Overview10分钟
Lesson 5 Overview10分钟
2 个练习
Lesson 4 Quiz8分钟
Lesson 5 Quiz8分钟
周
4完成时间为 4 小时
Week 4
10 个视频 （总计 57 分钟）, 1 个阅读材料, 2 个测验
10 个视频
6.2 Clustering Evaluation Measuring Clustering Quality2分钟
6.3 Constraint-Based Clustering4分钟
6.4 External Measures 1: Matching-Based Measures10分钟
6.5 External Measure 2: Entropy-Based Measures7分钟
6.6 External Measure 3: Pairwise Measures6分钟
6.7 Internal Measures for Clustering Validation7分钟
6.8 Relative Measures5分钟
6.9 Cluster Stability6分钟
6.10 Clustering Tendency5分钟
1 个阅读材料
Lesson 6 Overview10分钟
1 个练习
Lesson 6 Quiz8分钟
完成时间为 20 分钟
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
4.4
40 个审阅50%
完成这些课程后已开始新的职业生涯
33%
通过此课程获得实实在在的工作福利
50%
加薪或升职
热门审阅
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
A very good course, it gives me a general idea of how clustering algorithm work.
讲师
Jiawei Han
Abel Bliss ProfessorDepartment of Computer Science
领先获取学位
此 课程 隶属于 伊利诺伊大学香槟分校 提供的 100% 在线 Master in Computer Science。立即开始学习开放课程或专项课程，观看 iMBA 教师的课程并完成自主学习作业。
完成每门课程后，您将获得一个证书，您可以添加到 LinkedIn 和简历中。
如果申请并被录取参加全部课程，您的课程将计入您的学位学习进程。
关于 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.
关于 数据挖掘 专项课程
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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