Data Mining - Clustering and Association
Este Curso es parte de
Pathway en Introduction to Data Mining
Introducción al Curso
This course introduces basic concepts and methods of Data Mining with specific reference to Clustering and Association Rules. We present concept and purposes of cluster analysis, together with its’ main components. Partitioning, hierarchical, density based, and graph based clustering methods are described. Particular attention is devoted to; cluster validity measures and clustering validation. The last part of the course introduces association rule discovery. The concepts of association rule, frequent itemset, support and confidence are given. Furthermore, we give a brief description of the Apriori algorithm for frequent itemset generation, and introduce the concepts of maximal and closed frequent itemset. Finally, different criteria, for evaluating the quality of association patterns, are introduced.Informatica, Gestión y Análisis de datos
Horas de Entrenamiento40
NivelBeginner
Metodos de CursoTutoría
English
Duraciòn4 Semana
TipologíaOnline
Estado del CursoTutoría Soft
Agenda del Curso
Iniciar Suscripciones
Apertura del Curso
Comenzando la Tutoría
Tutoría Final
Tutoría Soft
Cierra Curso
Resultados de Aprendizaje
By the end of this course, you will be able to; develop a Data Mining workflow for solving a clustering problem as well as for extracting potentially interesting association rules. You will be able to use the appropriate proximity measure, and to select the "optimal clustering model" (whatever it means) to solve a clustering problem. Furthermore, you will be able to develop a Data Mining workflow to extract potentially interesting association rules. You will learn all this by using the KNIME open source platform, which integrates power and expressiveness of Weka, R and Java.
Conocimiento Recomendado
Basic knowledge of probability and statistics. Basic knowledge of R programming.
Libros de texto y lecturas recomendadas
- Pang-Ning Tan, Steinbach Michael and Vipin Kumar, (2006). Introduction to Data Mining. Morgan-Kaufmann.
- Kaufmann. Guojun Gan, Chaoqun Ma and Jianhong Wu (2007). Data Clustering: Theory, Algorithms, and Applications, Siam.
- Rui Xu and Donald C Wunsch II (2009). Clustering, Wiley.
Formato del Curso
The course spans four weeks. Each week requires 8 to 10 hours of work. Each week consists of 3 to 5 lectures. Each lecture consists of a methodology video, a software usage video and a practice session.Reglas para la obtención de certificados y Exámenes
Costo del Certificado de Participación
You must accomplish all practice sessions associated with lectures and upload the corresponding KNIME workflow to the course platform.
