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.
Este curso faz parte do Programa de cursos integrados Data Mining
Cluster Analysis in Data Mining
oferecido por
Informações sobre o curso
4.3
150 classificações
Habilidades que você terá
Cluster AnalysisData Clustering AlgorithmsK-Means ClusteringHierarchical Clustering
Programa - O que você aprenderá com este curso
Semana
1Course 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 leituras, 1 teste
1 vídeos
3 leituras
Syllabus10min
About the Discussion Forums10min
Social Media10min
1 exercício prático
Orientation Quiz10min
Module 1
13 videos (Total 65 min), 2 leituras, 2 testes
13 videos
1.2. Applications of Cluster Analysis2min
1.3 Requirements and Challenges5min
1.4 A Multi-Dimensional Categorization2min
1.5 An Overview of Typical Clustering Methodologies6min
1.6 An Overview of Clustering Different Types of Data6min
1.7 An Overview of User Insights and Clustering3min
2.1 Basic Concepts: Measuring Similarity between Objects3min
2.2 Distance on Numeric Data Minkowski Distance7min
2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables4min
2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types4min
2.5 Proximity Measure between Two Vectors Cosine Similarity2min
2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient13min
2 leituras
Lesson 1 Overview10min
Lesson 2 Overview10min
2 exercícios práticos
Lesson 1 Quiz8min
Lesson 2 Quiz12min
Semana
2Week 2
15 videos (Total 78 min), 3 leituras, 2 testes
15 videos
3.2 K-Means Clustering Method9min
3.3 Initialization of K-Means Clustering4min
3.4 The K-Medoids Clustering Method6min
3.5 The K-Medians and K-Modes Clustering Methods6min
3.6 Kernel K-Means Clustering8min
4.1 Hierarchical Clustering Methods1min
4.2 Agglomerative Clustering Algorithms8min
4.3 Divisive Clustering Algorithms3min
4.4 Extensions to Hierarchical Clustering3min
4.5 BIRCH: A Micro-Clustering-Based Approach7min
ClusterEnG Overview5min
ClusterEnG: K-Means and K-Medoids3min
ClusterEnG Application: AGNES4min
ClusterEnG Application: DBSCAN2min
3 leituras
Lesson 3 Overview10min
Lesson 4 Part 1 Overview10min
ClusterEnG Introduction10min
1 exercício prático
Lesson 3 Quiz10min
Semana
3Week 3
9 videos (Total 53 min), 2 leituras, 2 testes
9 videos
4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data8min
4.8 Probabilistic Hierarchical Clustering7min
5.1 Density-Based and Grid-Based Clustering Methods1min
5.2 DBSCAN: A Density-Based Clustering Algorithm8min
5.3 OPTICS: Ordering Points To Identify Clustering Structure9min
5.4 Grid-Based Clustering Methods3min
5.5 STING: A Statistical Information Grid Approach3min
5.6 CLIQUE: Grid-Based Subspace Clustering7min
2 leituras
Lesson 4 Part 2 Overview10min
Lesson 5 Overview10min
2 exercícios práticos
Lesson 4 Quiz8min
Lesson 5 Quiz8min
Semana
4Week 4
10 videos (Total 57 min), 1 leitura, 2 testes
10 videos
6.2 Clustering Evaluation Measuring Clustering Quality2min
6.3 Constraint-Based Clustering4min
6.4 External Measures 1: Matching-Based Measures10min
6.5 External Measure 2: Entropy-Based Measures7min
6.6 External Measure 3: Pairwise Measures6min
6.7 Internal Measures for Clustering Validation7min
6.8 Relative Measures5min
6.9 Cluster Stability6min
6.10 Clustering Tendency5min
1 leituras
Lesson 6 Overview10min
1 exercício prático
Lesson 6 Quiz8min
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
83%
consegui um benefício significativo de carreira com este curso
50%
recebi um aumento ou promoção
Melhores avaliações
por DD•Sep 25th 2017
A very good course, it gives me a general idea of how clustering algorithm work.
por TK•Oct 10th 2017
Very intense and required complex thinking and programming skill
Instrutores
Jiawei Han
Abel Bliss ProfessorDepartment of Computer Science
Start working towards your Master's degree
This curso 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.
Sobre 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.
Sobre o Programa de cursos integrados Data Mining
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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