Clustering and Association

Parte di

Pathway in Introduction to Data Mining


Learn how to formulate and solve Clustering and Association Rule extraction problems for use in Data Mining and Business Intelligence applications. Clustering and Association Rule extraction are potentially interesting to solve problems as; store layout, customer profiling, targeted marketing, market basket analysis, ... You will learn how to develop, validate and apply Data Mining workflows to solve clustering and association rule extraction problems. The course is self-contained and hands-on lectures are based on the KNIME open source software platform.


Data Mining: Clustering and Association

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.

By the end of this course, you will be able to; develop a Data Mining workflow for solving a custering 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.

Basic knowledge of probability and statistics. Basic knowledge of R programming.

1) Pang-Ning Tan, Steinbach Michael and Vipin Kumar, (2006). Introduction to Data Mining. Morgan-Kaufmann.  2) Rui Xu and Donald C Wunsch II (2009). Clustering, Wiley..  3) Kaufmann. Guojun Gan, Chaoqun Ma and Jianhong Wu (2007). DataClustering: Theory, Algorithms, and Applications, Siam.

The course spans four weeks. Each week requires 5 to 6 hours of work. Each week consists of 5 to 7 video-lectures. Each video-lecture consists of a methodology video, a software usage video and a practice session.

You must accomplish all practice sessions associated with lectures and upload the corresponding KNIME workflow to the course platform.
Modalità Corso
Tutoraggio
Stato del corso
Tutoraggio Soft
Durata
4 weeks
Impegno
8/10 hours a week
Categoria
Informatica, Gestione e Analisi Dati
Lingua
Inglese
Tipo
Online
Livello
Base
Avvio Iscrizioni
3 Ott 2016
Apertura Corso
3 Ott 2016

Partecipazione e Attestati

Quota di iscrizione
GRATUITO!

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