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.
This course is part of the Data Mining Specialization
Cluster Analysis in Data Mining
Offered By
About this Course
16,554 recent views
Skills you will gain
Cluster AnalysisData Clustering AlgorithmsK-Means ClusteringHierarchical Clustering
Learners taking this Course are
- Biostatisticians
- Data Scientists
- Data Analysts
- Risk Managers
- Machine Learning Engineers
Syllabus - What you will learn from this course
Week
11 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 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
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
Week
25 hours to complete
Week 2
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
Week
31 hour to complete
Week 3
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
Week
44 hours to complete
Week 4
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.
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Top reviews from Cluster Analysis in Data Mining
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
A very good course, it gives me a general idea of how clustering algorithm work.
Instructor
Jiawei Han
Abel Bliss ProfessorDepartment of Computer Science
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.
About 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.
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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