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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

44,168 already enrolled

Included with

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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

91%

of learners achieved a positive career outcome

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Build your subject-matter expertise

This course is part of the Data Mining Specialization

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

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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.

Module details

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