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

43,926 already enrolled

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6 modules

Gain insight into a topic and learn the fundamentals.

410 reviews

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

88%

Most learners liked this course

6 modules

Gain insight into a topic and learn the fundamentals.

410 reviews

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

88%

Most learners liked this course

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments

Taught in English

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This course is part of the Data Mining Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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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

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Instructor

Instructor ratings

(27 ratings)

Jiawei Han

University of Illinois Urbana-Champaign

4 Courses 72,085 learners

Offered by

University of Illinois Urbana-Champaign

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Showing 3 of 410

Reviewed on Jan 24, 2021

The material is too general, does not provide examples. So it's difficult when doing the exam.

Reviewed on Sep 24, 2017

A very good course, it gives me a general idea of how clustering algorithm work.

Reviewed on Sep 6, 2017

Very detailed introduction of Clustering techniques.

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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.