Cluster Analysis in Data Mining
Beschreibung
When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
About 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.
Created by: University of Illinois at Urbana-Champaign-
Taught by: Jiawei Han, Abel Bliss Professor
Department of Computer Science
Frequently asked questions
Es wurden noch keine FAQ hinterlegt. Falls Sie Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice. Wir helfen gerne weiter!
When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
About 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.
Created by: University of Illinois at Urbana-Champaign-
Taught by: Jiawei Han, Abel Bliss Professor
Department of Computer Science
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from your peersConnect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
CertificatesEarn official recognition for your work, and share your success with friends, colleagues, and employers.
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.Syllabus
WEEK 1
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, 3 readings, 1 practice quiz expand
- Video: Course Introduction
- Reading: Syllabus
- Reading: About the Discussion Forums
- Discussion Prompt: Getting to Know Your Classmates
- Reading: Social Media
- Practice Quiz: Orientation Quiz
Module 1
13 videos, 2 readings expand
- Reading: Lesson 1 Overview
- Video: 1.1. What is Cluster Analysis
- Video: 1.2. Applications of Cluster Analysis
- Video: 1.3 Requirements and Challenges
- Video: 1.4 A Multi-Dimensional Categorization
- Video: 1.5 An Overview of Typical Clustering Methodologies
- Video: 1.6 An Overview of Clustering Different Types of Data
- Video: 1.7 An Overview of User Insights and Clustering
- Reading: Lesson 2 Overview
- Video: 2.1 Basic Concepts: Measuring Similarity between Objects
- Video: 2.2 Distance on Numeric Data Minkowski Distance
- Video: 2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables
- Video: 2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types
- Video: 2.5 Proximity Measure between Two Vectors Cosine Similarity
- Video: 2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient
Graded: Lesson 1 Quiz
Graded: Lesson 2 Quiz
WEEK 2
Week 2
15 videos, 3 readings expand
- Reading: Lesson 3 Overview
- Video: 3.1 Partitioning-Based Clustering Methods
- Video: 3.2 K-Means Clustering Method
- Video: 3.3 Initialization of K-Means Clustering
- Video: 3.4 The K-Medoids Clustering Method
- Video: 3.5 The K-Medians and K-Modes Clustering Methods
- Video: 3.6 Kernel K-Means Clustering
- Reading: Lesson 4 Part 1 Overview
- Video: 4.1 Hierarchical Clustering Methods
- Video: 4.2 Agglomerative Clustering Algorithms
- Video: 4.3 Divisive Clustering Algorithms
- Video: 4.4 Extensions to Hierarchical Clustering
- Video: 4.5 BIRCH: A Micro-Clustering-Based Approach
- Reading: ClusterEnG Introduction
- Video: ClusterEnG Overview
- Video: ClusterEnG: K-Means and K-Medoids
- Video: ClusterEnG Application: AGNES
- Video: ClusterEnG Application: DBSCAN
Graded: Lesson 3 Quiz
Graded: Implementing the K-means Clustering Algorithm
WEEK 3
Week 3
9 videos, 2 readings expand
- Reading: Lesson 4 Part 2 Overview
- Video: 4.6 CURE: Clustering Using Well-Scattered Representatives
- Video: 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
- Video: 4.8 Probabilistic Hierarchical Clustering
- Reading: Lesson 5 Overview
- Video: 5.1 Density-Based and Grid-Based Clustering Methods
- Video: 5.2 DBSCAN: A Density-Based Clustering Algorithm
- Video: 5.3 OPTICS: Ordering Points To Identify Clustering Structure
- Video: 5.4 Grid-Based Clustering Methods
- Video: 5.5 STING: A Statistical Information Grid Approach
- Video: 5.6 CLIQUE: Grid-Based Subspace Clustering
Graded: Lesson 4 Quiz
Graded: Lesson 5 Quiz
WEEK 4
Week 4
10 videos, 1 reading expand
- Reading: Lesson 6 Overview
- Video: 6.1 Methods for Clustering Validation
- Video: 6.2 Clustering Evaluation Measuring Clustering Quality
- Video: 6.3 Constraint-Based Clustering
- Video: 6.4 External Measures 1: Matching-Based Measures
- Video: 6.5 External Measure 2: Entropy-Based Measures
- Video: 6.6 External Measure 3: Pairwise Measures
- Video: 6.7 Internal Measures for Clustering Validation
- Video: 6.8 Relative Measures
- Video: 6.9 Cluster Stability
- Video: 6.10 Clustering Tendency
Graded: Lesson 6 Quiz
Graded: Implementing Clustering Validation Measures
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
1 item expand
- Discussion Prompt: Final Reflections
Werden Sie über neue Bewertungen benachrichtigt
Schreiben Sie eine Bewertung
Haben Sie Erfahrung mit diesem Kurs? Schreiben Sie jetzt eine Bewertung und helfen Sie Anderen dabei die richtige Weiterbildung zu wählen. Als Dankeschön spenden wir € 1,00 an Stiftung Edukans.Es wurden noch keine FAQ hinterlegt. Falls Sie Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice. Wir helfen gerne weiter!