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.
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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
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
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Start instantly and learn at your own schedule.
Course 5 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 13 hours to complete
English
Subtitles: English, Korean
Offered by
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.
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
1 hour to complete
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 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 Quiz10m
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 Quiz8m
Lesson 2 Quiz12m
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 Quiz10m
1 hour to complete
Week 3
1 hour 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 Quiz8m
Lesson 5 Quiz8m
4 hours to complete
Week 4
4 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 Quiz8m
20 minutes to complete
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
20 minutes to complete
Reviews
4.5
TOP REVIEWS FROM CLUSTER ANALYSIS IN DATA MINING
by GLJan 25, 2018
This is a very good course covering all area of clustering. The only thing I feel a little struggle is some algorithm explained too brief, I prefer some detail step by step examples.
by DBMar 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.
by ESDec 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.
by UGApr 27, 2019
Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.
by VBNov 6, 2019
Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks
by ASDec 15, 2019
Good course. Some of the slides have value errors. Explanations for the programming assignments could be better.
by ABNov 6, 2016
The course is very insightful and very helpful for the data mining studies at university courses.
by DDSep 24, 2017
A very good course, it gives me a general idea of how clustering algorithm work.
by TKOct 9, 2017
Very intense and required complex thinking and programming skill
by SSSep 6, 2017
Very detailed introduction of Clustering techniques.
About the Data Mining Specialization
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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