Course Summary
Learn how to analyze data using cluster analysis, a powerful technique for grouping similar objects into clusters based on their attributes.Key Learning Points
- Understand the fundamentals of cluster analysis and its applications
- Explore different clustering algorithms and their strengths and weaknesses
- Learn how to evaluate and interpret cluster analysis results
Job Positions & Salaries of people who have taken this course might have
- USA: $120,000
- India: ₹12,00,000
- Spain: €45,000
- USA: $120,000
- India: ₹12,00,000
- Spain: €45,000
- USA: $85,000
- India: ₹8,00,000
- Spain: €30,000
- USA: $120,000
- India: ₹12,00,000
- Spain: €45,000
- USA: $85,000
- India: ₹8,00,000
- Spain: €30,000
- USA: $65,000
- India: ₹6,00,000
- Spain: €25,000
Related Topics for further study
Learning Outcomes
- Understand the theory and practical applications of cluster analysis
- Learn how to choose the right clustering algorithm for your data
- Develop the skills to interpret and communicate cluster analysis results
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics and data analysis
- Experience with a programming language such as Python or R
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video-based
Similar Courses
- Data Mining and Analysis
- Applied Data Science: Machine Learning
- Statistics with R
Related Education Paths
Related Books
Description
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.
Outline
- Course Orientation
- Course Introduction
- Syllabus
- About the Discussion Forums
- Social Media
- Orientation Quiz
- Module 1
- 1.1. What is Cluster Analysis
- 1.2. Applications of Cluster Analysis
- 1.3 Requirements and Challenges
- 1.4 A Multi-Dimensional Categorization
- 1.5 An Overview of Typical Clustering Methodologies
- 1.6 An Overview of Clustering Different Types of Data
- 1.7 An Overview of User Insights and Clustering
- 2.1 Basic Concepts: Measuring Similarity between Objects
- 2.2 Distance on Numeric Data Minkowski Distance
- 2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables
- 2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types
- 2.5 Proximity Measure between Two Vectors Cosine Similarity
- 2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient
- Lesson 1 Overview
- Lesson 2 Overview
- Lesson 1 Quiz
- Lesson 2 Quiz
- Week 2
- 3.1 Partitioning-Based Clustering Methods
- 3.2 K-Means Clustering Method
- 3.3 Initialization of K-Means Clustering
- 3.4 The K-Medoids Clustering Method
- 3.5 The K-Medians and K-Modes Clustering Methods
- 3.6 Kernel K-Means Clustering
- 4.1 Hierarchical Clustering Methods
- 4.2 Agglomerative Clustering Algorithms
- 4.3 Divisive Clustering Algorithms
- 4.4 Extensions to Hierarchical Clustering
- 4.5 BIRCH: A Micro-Clustering-Based Approach
- ClusterEnG Overview
- ClusterEnG: K-Means and K-Medoids
- ClusterEnG Application: AGNES
- ClusterEnG Application: DBSCAN
- Lesson 3 Overview
- Lesson 4 Part 1 Overview
- ClusterEnG Introduction
- Lesson 3 Quiz
- Week 3
- 4.6 CURE: Clustering Using Well-Scattered Representatives
- 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
- 4.8 Probabilistic Hierarchical Clustering
- 5.1 Density-Based and Grid-Based Clustering Methods
- 5.2 DBSCAN: A Density-Based Clustering Algorithm
- 5.3 OPTICS: Ordering Points To Identify Clustering Structure
- 5.4 Grid-Based Clustering Methods
- 5.5 STING: A Statistical Information Grid Approach
- 5.6 CLIQUE: Grid-Based Subspace Clustering
- Lesson 4 Part 2 Overview
- Lesson 5 Overview
- Lesson 4 Quiz
- Lesson 5 Quiz
- Week 4
- 6.1 Methods for Clustering Validation
- 6.2 Clustering Evaluation Measuring Clustering Quality
- 6.3 Constraint-Based Clustering
- 6.4 External Measures 1: Matching-Based Measures
- 6.5 External Measure 2: Entropy-Based Measures
- 6.6 External Measure 3: Pairwise Measures
- 6.7 Internal Measures for Clustering Validation
- 6.8 Relative Measures
- 6.9 Cluster Stability
- 6.10 Clustering Tendency
- Lesson 6 Overview
- Lesson 6 Quiz
- Course Conclusion
Summary of User Reviews
Discover the art of Cluster Analysis with this online course on Coursera. This course has received positive reviews from students who have found it to be a comprehensive and easy-to-follow guide to understanding Cluster Analysis.Key Aspect Users Liked About This Course
Many users thought the course was well-structured and provided valuable insights into the concept of Cluster Analysis.Pros from User Reviews
- The course is well-structured and easy to follow.
- The instructors provide valuable insights and examples.
- The assignments are challenging, yet manageable.
- The course covers a wide range of topics related to Cluster Analysis.
- The quizzes and exercises help reinforce the concepts learned.
Cons from User Reviews
- Some users found the course to be too basic.
- The course may not be suitable for those with advanced knowledge of Cluster Analysis.
- The course can be time-consuming, with some assignments taking several hours to complete.
- The course may not be suitable for those who prefer hands-on, practical learning.
- Some users found the course content to be too theoretical.
Jiawei Han
4.5 Raiting
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