Cluster Analysis in Data Mining

Cluster Analysis in Data Mining

This course is part of Data Mining Specialization

Instructor: Jiawei Han

Skills you'll gain

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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 videoTotal 6 minutes

Course Introduction6 minutesPreview module

3 readingsTotal 30 minutes

Syllabus10 minutes
About the Discussion Forums10 minutes
Social Media10 minutes

1 assignmentTotal 30 minutes

Orientation Quiz30 minutes

1 discussion promptTotal 10 minutes

Getting to Know Your Classmates10 minutes

1 pluginTotal 15 minutes

Welcome! Please tell us about yourself.15 minutes

What's included

13 videos2 readings2 assignments

13 videosTotal 65 minutes

1.1. What is Cluster Analysis2 minutesPreview module
1.2. Applications of Cluster Analysis2 minutes
1.3 Requirements and Challenges5 minutes
1.4 A Multi-Dimensional Categorization2 minutes
1.5 An Overview of Typical Clustering Methodologies6 minutes
1.6 An Overview of Clustering Different Types of Data6 minutes
1.7 An Overview of User Insights and Clustering3 minutes
2.1 Basic Concepts: Measuring Similarity between Objects3 minutes
2.2 Distance on Numeric Data Minkowski Distance7 minutes
2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables4 minutes
2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types4 minutes
2.5 Proximity Measure between Two Vectors Cosine Similarity2 minutes
2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient13 minutes

2 readingsTotal 20 minutes

Lesson 1 Overview10 minutes
Lesson 2 Overview10 minutes

2 assignmentsTotal 60 minutes

Lesson 1 Quiz30 minutes
Lesson 2 Quiz30 minutes

What's included

15 videos3 readings1 assignment1 programming assignment

15 videosTotal 77 minutes

3.1 Partitioning-Based Clustering Methods3 minutesPreview module
3.2 K-Means Clustering Method9 minutes
3.3 Initialization of K-Means Clustering4 minutes
3.4 The K-Medoids Clustering Method6 minutes
3.5 The K-Medians and K-Modes Clustering Methods6 minutes
3.6 Kernel K-Means Clustering8 minutes
4.1 Hierarchical Clustering Methods1 minute
4.2 Agglomerative Clustering Algorithms8 minutes
4.3 Divisive Clustering Algorithms3 minutes
4.4 Extensions to Hierarchical Clustering3 minutes
4.5 BIRCH: A Micro-Clustering-Based Approach7 minutes
ClusterEnG Overview5 minutes
ClusterEnG: K-Means and K-Medoids3 minutes
ClusterEnG Application: AGNES4 minutes
ClusterEnG Application: DBSCAN2 minutes

3 readingsTotal 30 minutes

Lesson 3 Overview10 minutes
Lesson 4 Part 1 Overview10 minutes
ClusterEnG Introduction10 minutes

1 assignmentTotal 30 minutes

Lesson 3 Quiz30 minutes

1 programming assignmentTotal 180 minutes

Implementing the K-means Clustering Algorithm180 minutes

What's included

9 videos2 readings2 assignments

9 videosTotal 53 minutes

4.6 CURE: Clustering Using Well-Scattered Representatives4 minutesPreview module
4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data8 minutes
4.8 Probabilistic Hierarchical Clustering7 minutes
5.1 Density-Based and Grid-Based Clustering Methods1 minute
5.2 DBSCAN: A Density-Based Clustering Algorithm8 minutes
5.3 OPTICS: Ordering Points To Identify Clustering Structure9 minutes
5.4 Grid-Based Clustering Methods3 minutes
5.5 STING: A Statistical Information Grid Approach3 minutes
5.6 CLIQUE: Grid-Based Subspace Clustering7 minutes

2 readingsTotal 20 minutes

Lesson 4 Part 2 Overview10 minutes
Lesson 5 Overview10 minutes

2 assignmentsTotal 60 minutes

Lesson 4 Quiz30 minutes
Lesson 5 Quiz30 minutes

What's included

10 videos1 reading1 assignment1 programming assignment

10 videosTotal 57 minutes

6.1 Methods for Clustering Validation1 minutePreview module
6.2 Clustering Evaluation Measuring Clustering Quality2 minutes
6.3 Constraint-Based Clustering4 minutes
6.4 External Measures 1: Matching-Based Measures10 minutes
6.5 External Measure 2: Entropy-Based Measures7 minutes
6.6 External Measure 3: Pairwise Measures6 minutes
6.7 Internal Measures for Clustering Validation7 minutes
6.8 Relative Measures5 minutes
6.9 Cluster Stability6 minutes
6.10 Clustering Tendency5 minutes

1 readingTotal 10 minutes

Lesson 6 Overview10 minutes

1 assignmentTotal 30 minutes

Lesson 6 Quiz30 minutes

1 programming assignmentTotal 180 minutes

Implementing Clustering Validation Measures180 minutes

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

What's included

1 discussion prompt1 plugin

Instructor

Instructor ratings

4.1 (26 ratings)

Jiawei Han

University of Illinois Urbana-Champaign

4 Courses67,972 learners

Offered by

University of Illinois Urbana-Champaign

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Reviewed on Nov 6, 2019

Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks

Reviewed on Aug 26, 2023

A tough course regarding programming assignment and few quiz.

Reviewed on Dec 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.

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