Acerca de este Curso

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Resultados profesionales del estudiante

25%

comenzó una nueva carrera después de completar estos cursos

17%

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

33%

consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Aprox. 14 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano

Habilidades que obtendrás

Cluster AnalysisData Clustering AlgorithmsK-Means ClusteringHierarchical Clustering

Resultados profesionales del estudiante

25%

comenzó una nueva carrera después de completar estos cursos

17%

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

33%

consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Aprox. 14 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano

Instructor

ofrecido por

Logotipo de Universidad de Illinois en Urbana-Champaign

Universidad de Illinois en Urbana-Champaign

Comienza a trabajar para obtener tu maestría

Este curso es parte del Master in Computer Science completamente en línea de Universidad de Illinois en Urbana-Champaign. Si eres aceptado en el programa completo, tus cursos cuentan para tu título.

Programa - Qué aprenderás en este curso

Calificación del contenidoThumbs Up89%(1,145 calificaciones)Info
Semana
1

Semana 1

1 hora para completar

Course Orientation

1 hora para completar
1 video (Total 7 minutos), 3 lecturas, 1 cuestionario
1 video
3 lecturas
Syllabus10m
About the Discussion Forums10m
Social Media10m
1 ejercicio de práctica
Orientation Quiz10m
2 horas para completar

Module 1

2 horas para completar
13 videos (Total 65 minutos), 2 lecturas, 2 cuestionarios
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 lecturas
Lesson 1 Overview10m
Lesson 2 Overview10m
2 ejercicios de práctica
Lesson 1 Quiz8m
Lesson 2 Quiz12m
Semana
2

Semana 2

5 horas para completar

Week 2

5 horas para completar
15 videos (Total 78 minutos), 3 lecturas, 2 cuestionarios
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 lecturas
Lesson 3 Overview10m
Lesson 4 Part 1 Overview10m
ClusterEnG Introduction10m
1 ejercicio de práctica
Lesson 3 Quiz10m
Semana
3

Semana 3

1 hora para completar

Week 3

1 hora para completar
9 videos (Total 53 minutos), 2 lecturas, 2 cuestionarios
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 lecturas
Lesson 4 Part 2 Overview10m
Lesson 5 Overview10m
2 ejercicios de práctica
Lesson 4 Quiz8m
Lesson 5 Quiz8m
Semana
4

Semana 4

4 horas para completar

Week 4

4 horas para completar
10 videos (Total 57 minutos), 1 lectura, 2 cuestionarios
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 lectura
Lesson 6 Overview10m
1 ejercicio de práctica
Lesson 6 Quiz8m
20 minutos para completar

Course Conclusion

20 minutos para completar

Revisiones

Principales revisiones sobre CLUSTER ANALYSIS IN DATA MINING

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Acerca de Programa especializado: minería de datos

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....
minería de datos

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