Acerca de este Curso
4.3
142 ratings
32 reviews
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....
Globe

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Calendar

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Clock

Sugerido: 8 hours/week

Aprox. 16 horas para completar
Comment Dots

English

Subtítulos: English

Habilidades que obtendrás

Cluster AnalysisData Clustering AlgorithmsK-Means ClusteringHierarchical Clustering
Globe

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Calendar

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Clock

Sugerido: 8 hours/week

Aprox. 16 horas para completar
Comment Dots

English

Subtítulos: English

Programa - Qué aprenderás en este curso

1

Sección
Clock
1 hora para completar

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....
Reading
1 video (Total: 7 min), 3 readings, 1 quiz
Video1 video
Reading3 lecturas
Syllabus10m
About the Discussion Forums10m
Social Media10m
Quiz1 ejercicio de práctica
Orientation Quiz10m
Clock
2 horas para completar

Module 1

...
Reading
13 videos (Total: 65 min), 2 readings, 2 quizzes
Video13 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
Reading2 lecturas
Lesson 1 Overview10m
Lesson 2 Overview10m
Quiz2 ejercicios de práctica
Lesson 1 Quiz8m
Lesson 2 Quiz12m

2

Sección
Clock
5 horas para completar

Week 2

...
Reading
15 videos (Total: 78 min), 3 readings, 2 quizzes
Video15 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
Reading3 lecturas
Lesson 3 Overview10m
Lesson 4 Part 1 Overview10m
ClusterEnG Introduction10m
Quiz1 ejercicio de práctica
Lesson 3 Quiz10m

3

Sección
Clock
1 hora para completar

Week 3

...
Reading
9 videos (Total: 53 min), 2 readings, 2 quizzes
Video9 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
Reading2 lecturas
Lesson 4 Part 2 Overview10m
Lesson 5 Overview10m
Quiz2 ejercicios de práctica
Lesson 4 Quiz8m
Lesson 5 Quiz8m

4

Sección
Clock
4 horas para completar

Week 4

...
Reading
10 videos (Total: 57 min), 1 reading, 2 quizzes
Video10 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
Reading1 lectura
Lesson 6 Overview10m
Quiz1 ejercicio de práctica
Lesson 6 Quiz8m
Clock
5 minutos para completar

Course Conclusion

In the course conclusion, feel free to share any thoughts you have on this course experience....
Reading
4.3
Briefcase

83%

consiguió un beneficio tangible en su carrera profesional gracias a este curso
Money

50%

consiguió un aumento de sueldo o ascenso

Principales revisiones

por DDSep 25th 2017

A very good course, it gives me a general idea of how clustering algorithm work.

por TKOct 10th 2017

Very intense and required complex thinking and programming skill

Instructor

Jiawei Han

Abel Bliss Professor
Department of Computer Science

Acerca de 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. ...

Acerca del programa especializado 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....
Data Mining

Preguntas Frecuentes

  • 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.

  • 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.

¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.