Cluster Analysis in Data Mining
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Cluster Analysis in Data Mining
This course is part of Data Mining Specialization
Instructor: Jiawei Han
44,271 already enrolled
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There are 6 modules in this course
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.
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.
What's included
1 video3 readings1 assignment1 discussion prompt1 plugin
1 video•Total 7 minutes
- Course Introduction•7 minutes
3 readings•Total 30 minutes
- Syllabus•10 minutes
- About the Discussion Forums•10 minutes
- Social Media•10 minutes
1 assignment•Total 30 minutes
- Orientation Quiz•30 minutes
1 discussion prompt•Total 10 minutes
- Getting to Know Your Classmates•10 minutes
1 plugin•Total 15 minutes
- Welcome! Please tell us about yourself.•15 minutes
What's included
13 videos2 readings2 assignments
13 videos•Total 65 minutes
- 1.1. What is Cluster Analysis•2 minutes
- 1.2. Applications of Cluster Analysis•2 minutes
- 1.3 Requirements and Challenges•5 minutes
- 1.4 A Multi-Dimensional Categorization•2 minutes
- 1.5 An Overview of Typical Clustering Methodologies•7 minutes
- 1.6 An Overview of Clustering Different Types of Data•7 minutes
- 1.7 An Overview of User Insights and Clustering•3 minutes
- 2.1 Basic Concepts: Measuring Similarity between Objects•3 minutes
- 2.2 Distance on Numeric Data Minkowski Distance•7 minutes
- 2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables•5 minutes
- 2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types•4 minutes
- 2.5 Proximity Measure between Two Vectors Cosine Similarity•3 minutes
- 2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient•14 minutes
2 readings•Total 20 minutes
- Lesson 1 Overview•10 minutes
- Lesson 2 Overview•10 minutes
2 assignments•Total 60 minutes
- Lesson 1 Quiz•30 minutes
- Lesson 2 Quiz•30 minutes
What's included
15 videos3 readings1 assignment1 programming assignment
15 videos•Total 78 minutes
- 3.1 Partitioning-Based Clustering Methods•3 minutes
- 3.2 K-Means Clustering Method•9 minutes
- 3.3 Initialization of K-Means Clustering•5 minutes
- 3.4 The K-Medoids Clustering Method•7 minutes
- 3.5 The K-Medians and K-Modes Clustering Methods•6 minutes
- 3.6 Kernel K-Means Clustering•8 minutes
- 4.1 Hierarchical Clustering Methods•2 minutes
- 4.2 Agglomerative Clustering Algorithms•8 minutes
- 4.3 Divisive Clustering Algorithms•3 minutes
- 4.4 Extensions to Hierarchical Clustering•3 minutes
- 4.5 BIRCH: A Micro-Clustering-Based Approach•7 minutes
- ClusterEnG Overview•5 minutes
- ClusterEnG: K-Means and K-Medoids•3 minutes
- ClusterEnG Application: AGNES•4 minutes
- ClusterEnG Application: DBSCAN•3 minutes
3 readings•Total 30 minutes
- Lesson 3 Overview•10 minutes
- Lesson 4 Part 1 Overview•10 minutes
- ClusterEnG Introduction•10 minutes
1 assignment•Total 30 minutes
- Lesson 3 Quiz•30 minutes
1 programming assignment•Total 180 minutes
- Implementing the K-means Clustering Algorithm•180 minutes
What's included
9 videos2 readings2 assignments
9 videos•Total 53 minutes
- 4.6 CURE: Clustering Using Well-Scattered Representatives•5 minutes
- 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data•8 minutes
- 4.8 Probabilistic Hierarchical Clustering•7 minutes
- 5.1 Density-Based and Grid-Based Clustering Methods•2 minutes
- 5.2 DBSCAN: A Density-Based Clustering Algorithm•8 minutes
- 5.3 OPTICS: Ordering Points To Identify Clustering Structure•9 minutes
- 5.4 Grid-Based Clustering Methods•3 minutes
- 5.5 STING: A Statistical Information Grid Approach•4 minutes
- 5.6 CLIQUE: Grid-Based Subspace Clustering•7 minutes
2 readings•Total 20 minutes
- Lesson 4 Part 2 Overview•10 minutes
- Lesson 5 Overview•10 minutes
2 assignments•Total 60 minutes
- Lesson 4 Quiz•30 minutes
- Lesson 5 Quiz•30 minutes
What's included
10 videos1 reading1 assignment1 programming assignment
10 videos•Total 57 minutes
- 6.1 Methods for Clustering Validation•1 minute
- 6.2 Clustering Evaluation Measuring Clustering Quality•3 minutes
- 6.3 Constraint-Based Clustering•5 minutes
- 6.4 External Measures 1: Matching-Based Measures•10 minutes
- 6.5 External Measure 2: Entropy-Based Measures•7 minutes
- 6.6 External Measure 3: Pairwise Measures•6 minutes
- 6.7 Internal Measures for Clustering Validation•7 minutes
- 6.8 Relative Measures•6 minutes
- 6.9 Cluster Stability•7 minutes
- 6.10 Clustering Tendency•5 minutes
1 reading•Total 10 minutes
- Lesson 6 Overview•10 minutes
1 assignment•Total 30 minutes
- Lesson 6 Quiz•30 minutes
1 programming assignment•Total 180 minutes
- Implementing Clustering Validation Measures•180 minutes
In the course conclusion, feel free to share any thoughts you have on this course experience.
What's included
1 discussion prompt1 plugin
1 discussion prompt•Total 10 minutes
- Final Reflections•10 minutes
1 plugin•Total 15 minutes
- How was the course?•15 minutes
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Reviewed on Jan 24, 2021
The material is too general, does not provide examples. So it's difficult when doing the exam.
Reviewed on Apr 27, 2019
Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.
Reviewed on Aug 26, 2023
A tough course regarding programming assignment and few quiz.
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