Cluster Analysis in Data Mining

Cluster Analysis in Data Mining

This course is part of Data Mining Specialization

Instructor: Jiawei Han

Access provided by Universidade Federal de Lavras UFLA

44,449 already enrolled

6 modules

Gain insight into a topic and learn the fundamentals.

411 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.

411 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

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Assessments

7 assignments

Taught in English

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This course is part of the Data Mining 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

What's included

13 videos2 readings2 assignments

13 videosTotal 65 minutes

1.1. What is Cluster Analysis2 minutes
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 Methodologies7 minutes
1.6 An Overview of Clustering Different Types of Data7 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 Variables5 minutes
2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types4 minutes
2.5 Proximity Measure between Two Vectors Cosine Similarity3 minutes
2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient14 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 78 minutes

3.1 Partitioning-Based Clustering Methods3 minutes
3.2 K-Means Clustering Method9 minutes
3.3 Initialization of K-Means Clustering5 minutes
3.4 The K-Medoids Clustering Method7 minutes
3.5 The K-Medians and K-Modes Clustering Methods6 minutes
3.6 Kernel K-Means Clustering8 minutes
4.1 Hierarchical Clustering Methods2 minutes
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: DBSCAN3 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 Representatives5 minutes
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 Methods2 minutes
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 Approach4 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 minute
6.2 Clustering Evaluation Measuring Clustering Quality3 minutes
6.3 Constraint-Based Clustering5 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 Measures6 minutes
6.9 Cluster Stability7 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

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Instructor

Instructor ratings

(27 ratings)

Jiawei Han

University of Illinois Urbana-Champaign

4 Courses73,114 learners

Offered by

University of Illinois Urbana-Champaign

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Showing 3 of 411

4.0

Reviewed on Sep 6, 2017

Very detailed introduction of Clustering techniques.

5.0

Reviewed on Sep 18, 2017

Very informative lectures, wonderful assignments. This course isn't so easy but it gives you real knowledge and useful experience.

4.0

Reviewed on Mar 9, 2019

Useful theory. It will be challenging for non-math students. and also lecturer's native language influence iis going to be challening as well to follow along.

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