Date/Time
Date(s) - 21/05/2018 - 25/05/2018
All Day
Location
TBA
Joint Summer School ERS-IASC, ECAS and SIS-CLADAG
Clustering, Data Analysis and Visualization of Complex Data
May 21-25, 2018, Catania (Italy)
The course is intended to achieve postgraduate training in special areas of statistics for both researchers and professional data analysts. The focus is on classification and clustering methods with particular emphasis on modern high-dimensional data sets (MHDS). MHDS have recently emerged because of the fast improvement in data acquisition, storage and processing. The availability of massive data sets are of large interest also in machine learning, data science and computer science. It applies in many contexts such as biological experiments, financial markets, astronomy, etc. Classification and clustering play a key role in this new paradigm to discover the inhomogeneous structure often underlying these data. Starting from basic concepts, the course will introduce the audience to novel techniques and software through extensive applications to real data.
Numerical applications will be performed through a variety of software, including some R packages and some cloud-computing platforms (SaaS, Software as a Service) issuing from research but targeting many kinds of practitioners
Topics
1. Introduction to cluster analysis and classification
2. Mixture models, model-based clustering and algorithms.
3. Model selection, variable selection and cluster validation.
4. Further topics in cluster analysis and classification
5. Three issues in clustering big data: Co-clustering, clustering of high-dimensional data, clustering of time series
Lecturers
Christophe Biernacki (UFR de Mathématiques, Université Lille 1, France)
Charles Bouveyron (Université Nice Côte d’Azur & INRIA, France)
Pietro Coretto (Università di Salerno, Italy)
Sylvia Frühwirth Schnatter (Vienna University of Economics and Business, Austria)
Salvatore Ingrassia (Università di Catania, Italy)
More information available at http://www.clucla-summerschool.org/