À propos de ce cours : 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.
Créé par : Université de l'Illinois à Urbana-Champaign
Enseigné par : Jiawei Han, Abel Bliss Professor
Department of Computer Science
| Informations de base | Cours 5 de 6 dans Data Mining Specialization |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 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 vidéo, 3 lectures, 1 quiz pour s'exercer
- Vidéo: Course Introduction
- 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 vidéos, 2 lectures
- 阅读: Lesson 1 Overview
- Vidéo: 1.1. What is Cluster Analysis
- Vidéo: 1.2. Applications of Cluster Analysis
- Vidéo: 1.3 Requirements and Challenges
- Vidéo: 1.4 A Multi-Dimensional Categorization
- Vidéo: 1.5 An Overview of Typical Clustering Methodologies
- Vidéo: 1.6 An Overview of Clustering Different Types of Data
- Vidéo: 1.7 An Overview of User Insights and Clustering
- 阅读: Lesson 2 Overview
- Vidéo: 2.1 Basic Concepts: Measuring Similarity between Objects
- Vidéo: 2.2 Distance on Numeric Data Minkowski Distance
- Vidéo: 2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables
- Vidéo: 2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types
- Vidéo: 2.5 Proximity Measure between Two Vectors Cosine Similarity
- Vidéo: 2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient
Noté: Lesson 1 Quiz
Noté: Lesson 2 Quiz
SEMAINE 2
Week 2
15 vidéos, 3 lectures
- 阅读: Lesson 3 Overview
- Vidéo: 3.1 Partitioning-Based Clustering Methods
- Vidéo: 3.2 K-Means Clustering Method
- Vidéo: 3.3 Initialization of K-Means Clustering
- Vidéo: 3.4 The K-Medoids Clustering Method
- Vidéo: 3.5 The K-Medians and K-Modes Clustering Methods
- Vidéo: 3.6 Kernel K-Means Clustering
- 阅读: Lesson 4 Part 1 Overview
- Vidéo: 4.1 Hierarchical Clustering Methods
- Vidéo: 4.2 Agglomerative Clustering Algorithms
- Vidéo: 4.3 Divisive Clustering Algorithms
- Vidéo: 4.4 Extensions to Hierarchical Clustering
- Vidéo: 4.5 BIRCH: A Micro-Clustering-Based Approach
- 阅读: ClusterEnG Introduction
- Vidéo: ClusterEnG Overview
- Vidéo: ClusterEnG: K-Means and K-Medoids
- Vidéo: ClusterEnG Application: AGNES
- Vidéo: ClusterEnG Application: DBSCAN
Noté: Lesson 3 Quiz
Noté: Implementing the K-means Clustering Algorithm
SEMAINE 3
Week 3
9 vidéos, 2 lectures
- 阅读: Lesson 4 Part 2 Overview
- Vidéo: 4.6 CURE: Clustering Using Well-Scattered Representatives (Optional)
- Vidéo: 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
- Vidéo: 4.8 Probabilistic Hierarchical Clustering
- 阅读: Lesson 5 Overview
- Vidéo: 5.1 Density-Based and Grid-Based Clustering Methods
- Vidéo: 5.2 DBSCAN: A Density-Based Clustering Algorithm
- Vidéo: 5.3 OPTICS: Ordering Points To Identify Clustering Structure
- Vidéo: 5.4 Grid-Based Clustering Methods
- Vidéo: 5.5 STING: A Statistical Information Grid Approach
- Vidéo: 5.6 CLIQUE: Grid-Based Subspace Clustering
Noté: Lesson 4 Quiz
Noté: Lesson 5 Quiz
SEMAINE 4
Week 4
10 vidéos, 1 lecture
- 阅读: Lesson 6 Overview
- Vidéo: 6.1 Methods for Clustering Validation
- Vidéo: 6.2 Clustering Evaluation Measuring Clustering Quality
- Vidéo: 6.3 Constraint-Based Clustering
- Vidéo: 6.4 External Measures 1: Matching-Based Measures
- Vidéo: 6.5 External Measure 2: Entropy-Based Measures
- Vidéo: 6.6 External Measure 3: Pairwise Measures
- Vidéo: 6.7 Internal Measures for Clustering Validation
- Vidéo: 6.8 Relative Measures
- Vidéo: 6.9 Cluster Stability
- Vidéo: 6.10 Clustering Tendency
Noté: Lesson 6 Quiz
Noté: Implementing Clustering Validation Measures
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
1 élément
- 讨论提示: Final Reflections
FAQ
Comment cela fonctionne
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。
Créateurs
Université de l'Illinois à Urbana-Champaign
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
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.