This course is part of the Data Mining Specialization

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396 ratings

Jiawei Han

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Part of the Master of Computer Science degree
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Data Mining SpecializationUniversity of Illinois at Urbana-Champaign

About this Course

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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.

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Shareable Certificate

Earn a Certificate upon completion

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Course 5 of 6 in the

Data Mining Specialization

Approx. 17 hours to complete

English

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Skills you will gain

Cluster Analysis
Data Clustering Algorithms
K-Means Clustering
Hierarchical Clustering

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 5 of 6 in the

Data Mining Specialization

Approx. 17 hours to complete

English

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Try Coursera for Business

Instructor

Instructor rating

4.09/5 (23 Ratings)

Jiawei Han

Abel Bliss Professor

Department of Computer Science

61,636 Learners

4 Courses

Offered by

University of Illinois at Urbana-Champaign

Start working towards your Master's degree

This course is part of the 100% online Master of Computer Science from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Content Rating89%(1,312 ratings)

Week

Week 1

2 hours to complete

Course Orientation

2 hours to complete

1 video (Total 7 min), 3 readings, 1 quiz

2 hours to complete

Module 1

2 hours to complete

13 videos (Total 65 min), 2 readings, 2 quizzes

13 videos

1.1. What is Cluster Analysis2m

1.2. Applications of Cluster Analysis2m

1.3 Requirements and Challenges5m

1.4 A Multi-Dimensional Categorization2m

1.5 An Overview of Typical Clustering Methodologies6m

1.6 An Overview of Clustering Different Types of Data6m

1.7 An Overview of User Insights and Clustering3m

2.1 Basic Concepts: Measuring Similarity between Objects3m

2.2 Distance on Numeric Data Minkowski Distance7m

2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables4m

2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types4m

2.5 Proximity Measure between Two Vectors Cosine Similarity2m

2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient13m

2 readings

Lesson 1 Overview10m

Lesson 2 Overview10m

2 practice exercises

Lesson 1 Quiz30m

Lesson 2 Quiz30m

Week

Week 2

5 hours to complete

Week 2

5 hours to complete

15 videos (Total 78 min), 3 readings, 2 quizzes

15 videos

3.1 Partitioning-Based Clustering Methods3m

3.2 K-Means Clustering Method9m

3.3 Initialization of K-Means Clustering4m

3.4 The K-Medoids Clustering Method6m

3.5 The K-Medians and K-Modes Clustering Methods6m

3.6 Kernel K-Means Clustering8m

4.1 Hierarchical Clustering Methods1m

4.2 Agglomerative Clustering Algorithms8m

4.3 Divisive Clustering Algorithms3m

4.4 Extensions to Hierarchical Clustering3m

4.5 BIRCH: A Micro-Clustering-Based Approach7m

ClusterEnG Overview5m

ClusterEnG: K-Means and K-Medoids3m

ClusterEnG Application: AGNES4m

ClusterEnG Application: DBSCAN2m

3 readings

Lesson 3 Overview10m

Lesson 4 Part 1 Overview10m

ClusterEnG Introduction10m

1 practice exercise

Lesson 3 Quiz30m

Week

Week 3

2 hours to complete

Week 3

2 hours to complete

9 videos (Total 53 min), 2 readings, 2 quizzes

9 videos

4.6 CURE: Clustering Using Well-Scattered Representatives4m

4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data8m

4.8 Probabilistic Hierarchical Clustering7m

5.1 Density-Based and Grid-Based Clustering Methods1m

5.2 DBSCAN: A Density-Based Clustering Algorithm8m

5.3 OPTICS: Ordering Points To Identify Clustering Structure9m

5.4 Grid-Based Clustering Methods3m

5.5 STING: A Statistical Information Grid Approach3m

5.6 CLIQUE: Grid-Based Subspace Clustering7m

2 readings

Lesson 4 Part 2 Overview10m

Lesson 5 Overview10m

2 practice exercises

Lesson 4 Quiz30m

Lesson 5 Quiz30m

Week

Week 4

5 hours to complete

Week 4

5 hours to complete

10 videos (Total 57 min), 1 reading, 2 quizzes

10 videos

6.1 Methods for Clustering Validation1m

6.2 Clustering Evaluation Measuring Clustering Quality2m

6.3 Constraint-Based Clustering4m

6.4 External Measures 1: Matching-Based Measures10m

6.5 External Measure 2: Entropy-Based Measures7m

6.6 External Measure 3: Pairwise Measures6m

6.7 Internal Measures for Clustering Validation7m

6.8 Relative Measures5m

6.9 Cluster Stability6m

6.10 Clustering Tendency5m

1 reading

Lesson 6 Overview10m

1 practice exercise

Lesson 6 Quiz30m

25 minutes to complete

Course Conclusion

25 minutes to complete

Reviews

4.5

62 reviews

5 stars
66.41%
4 stars
23.48%
3 stars
5.55%
2 stars
2.02%
1 star
2.52%

TOP REVIEWS FROM CLUSTER ANALYSIS IN DATA MINING

by PRJul 27, 2020

Covers great deal of topics and various aspects of clustering

by ESDec 17, 2018

This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.

by GLJan 25, 2018

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.

by RGJan 24, 2021

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

View all reviews

About the Data Mining Specialization

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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.
What will I get if I subscribe to this Specialization?
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. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
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.

More questions? Visit the Learner Help Center.

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Cluster Analysis in Data Mining

About this Course

Skills you will gain