About this Course

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Learner Career Outcomes

25%

started a new career after completing these courses

17%

got a tangible career benefit from this course

33%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 5 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 16 hours to complete
English

Skills you will gain

Cluster AnalysisData Clustering AlgorithmsK-Means ClusteringHierarchical Clustering

Learner Career Outcomes

25%

started a new career after completing these courses

17%

got a tangible career benefit from this course

33%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 5 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 16 hours to complete
English

Offered by

Placeholder

University of Illinois at Urbana-Champaign

Start working towards your Master's degree

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

Syllabus - What you will learn from this course

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Week
1

Week 1

1 hour to complete

Course Orientation

1 hour to complete
1 video (Total 7 min), 3 readings, 1 quiz
1 video
3 readings
Syllabus10m
About the Discussion Forums10m
Social Media10m
1 practice exercise
Orientation Quiz30m
2 hours to complete

Module 1

2 hours to complete
13 videos (Total 65 min), 2 readings, 2 quizzes
13 videos
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
2

Week 2

5 hours to complete

Week 2

5 hours to complete
15 videos (Total 78 min), 3 readings, 2 quizzes
15 videos
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
3

Week 3

2 hours to complete

Week 3

2 hours to complete
9 videos (Total 53 min), 2 readings, 2 quizzes
9 videos
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
4

Week 4

5 hours to complete

Week 4

5 hours to complete
10 videos (Total 57 min), 1 reading, 2 quizzes
10 videos
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
20 minutes to complete

Course Conclusion

20 minutes to complete

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