Data Mining - Clustering and Association
Questo Corso è parte del
Pathway in Introduction to Data Mining
Introduzione al Corso
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, Gestione e Analisi dei Dati
Ore di Formazione40
LivelloBase
Modalità CorsoTutoraggio
English
Durata4 Settimane
TipologiaOnline
Stato del CorsoTutoraggio Soft
Agenda del Corso
Avvio Iscrizioni
Apertura Corso
Inizio Tutoraggio
Fine Tutoraggio
Tutoraggio Soft
Chiusura Corso
Risultati Attesi
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.
Pre-requisiti
Basic knowledge of probability and statistics. Basic knowledge of R programming.
Libri di testo e letture consigliate
- 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 corso
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.Regole per ottenere gli Attestati e sostenere gli Esami
Attestato di Partecipazione
You must accomplish all practice sessions associated with lectures and upload the corresponding KNIME workflow to the course platform.
