Cluster Analysis in Data Mining

Location type
Logo Coursera
Provider rating: starstarstarstar_borderstar_border 6.3 Coursera has an average rating of 6.3 (out of 4 reviews)

Need more information? Get more details on the site of the provider.

Description

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About 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.

Created by:  University of Illinois at Urbana-Champaign
  • Taught by:  Jiawei Han, Abel Bliss Professor

    Department of Computer Science
Basic Info Course 5 of 6 in the Data Mining Specialization Language English How To Pass…

Read the complete description

Frequently asked questions

There are no frequently asked questions yet.  

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About 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.

Created by:  University of Illinois at Urbana-Champaign
  • Taught by:  Jiawei Han, Abel Bliss Professor

    Department of Computer Science
Basic Info Course 5 of 6 in the Data Mining Specialization Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.1 stars Average User Rating 4.1See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from your peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

University of Illinois at 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.

Syllabus


WEEK 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 video, 3 readings, 1 practice quiz expand


  1. Video: Course Introduction
  2. Reading: Syllabus
  3. Reading: About the Discussion Forums
  4. Discussion Prompt: Getting to Know Your Classmates
  5. Reading: Social Media
  6. Practice Quiz: Orientation Quiz


Module 1



13 videos, 2 readings expand


  1. Reading: Lesson 1 Overview
  2. Video: 1.1. What is Cluster Analysis
  3. Video: 1.2. Applications of Cluster Analysis
  4. Video: 1.3 Requirements and Challenges
  5. Video: 1.4 A Multi-Dimensional Categorization
  6. Video: 1.5 An Overview of Typical Clustering Methodologies
  7. Video: 1.6 An Overview of Clustering Different Types of Data
  8. Video: 1.7 An Overview of User Insights and Clustering
  9. Reading: Lesson 2 Overview
  10. Video: 2.1 Basic Concepts: Measuring Similarity between Objects
  11. Video: 2.2 Distance on Numeric Data Minkowski Distance
  12. Video: 2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables
  13. Video: 2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types
  14. Video: 2.5 Proximity Measure between Two Vectors Cosine Similarity
  15. Video: 2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient

Graded: Lesson 1 Quiz
Graded: Lesson 2 Quiz

WEEK 2


Week 2



15 videos, 3 readings expand


  1. Reading: Lesson 3 Overview
  2. Video: 3.1 Partitioning-Based Clustering Methods
  3. Video: 3.2 K-Means Clustering Method
  4. Video: 3.3 Initialization of K-Means Clustering
  5. Video: 3.4 The K-Medoids Clustering Method
  6. Video: 3.5 The K-Medians and K-Modes Clustering Methods
  7. Video: 3.6 Kernel K-Means Clustering
  8. Reading: Lesson 4 Part 1 Overview
  9. Video: 4.1 Hierarchical Clustering Methods
  10. Video: 4.2 Agglomerative Clustering Algorithms
  11. Video: 4.3 Divisive Clustering Algorithms
  12. Video: 4.4 Extensions to Hierarchical Clustering
  13. Video: 4.5 BIRCH: A Micro-Clustering-Based Approach
  14. Reading: ClusterEnG Introduction
  15. Video: ClusterEnG Overview
  16. Video: ClusterEnG: K-Means and K-Medoids
  17. Video: ClusterEnG Application: AGNES
  18. Video: ClusterEnG Application: DBSCAN

Graded: Lesson 3 Quiz
Graded: Implementing the K-means Clustering Algorithm

WEEK 3


Week 3



9 videos, 2 readings expand


  1. Reading: Lesson 4 Part 2 Overview
  2. Video: 4.6 CURE: Clustering Using Well-Scattered Representatives
  3. Video: 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
  4. Video: 4.8 Probabilistic Hierarchical Clustering
  5. Reading: Lesson 5 Overview
  6. Video: 5.1 Density-Based and Grid-Based Clustering Methods
  7. Video: 5.2 DBSCAN: A Density-Based Clustering Algorithm
  8. Video: 5.3 OPTICS: Ordering Points To Identify Clustering Structure
  9. Video: 5.4 Grid-Based Clustering Methods
  10. Video: 5.5 STING: A Statistical Information Grid Approach
  11. Video: 5.6 CLIQUE: Grid-Based Subspace Clustering

Graded: Lesson 4 Quiz
Graded: Lesson 5 Quiz

WEEK 4


Week 4



10 videos, 1 reading expand


  1. Reading: Lesson 6 Overview
  2. Video: 6.1 Methods for Clustering Validation
  3. Video: 6.2 Clustering Evaluation Measuring Clustering Quality
  4. Video: 6.3 Constraint-Based Clustering
  5. Video: 6.4 External Measures 1: Matching-Based Measures
  6. Video: 6.5 External Measure 2: Entropy-Based Measures
  7. Video: 6.6 External Measure 3: Pairwise Measures
  8. Video: 6.7 Internal Measures for Clustering Validation
  9. Video: 6.8 Relative Measures
  10. Video: 6.9 Cluster Stability
  11. Video: 6.10 Clustering Tendency

Graded: Lesson 6 Quiz
Graded: Implementing Clustering Validation Measures

Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.


1 item expand


  1. Discussion Prompt: Final Reflections
There are no reviews yet.

Share your review

Do you have a learning experience with this course? Submit your review and help other people make the right choice. As a thank you for your effort we will donate $1.- to Stichting Edukans.

There are no frequently asked questions yet.