V. Batagelj(Slovenia) M. Ichino(Japan)
L. Billard(USA) S. Laaksonen(Finland)
H. Bock(Germany) T. Lee(Korea)
P. Brito(Portugal) P. Nagabushan (India)
V. Esposito Vinzi(France)
Vladimir Batagelj
University of Ljubljana, Ljubljana, Slovenia
R-package Clamix Clustering Symbolic Objects Described by Discrete Distributions [slides] [photo]
Lynne Billard
University of Georgia
Principal Component Analysis for Quantitative Symbolic Data [photo]
Paula Brito
Faculdade de Economia & LIAAD -INESC Porto LA, Universidade do Porto, Porto, Portugal
Conceptual Clustering of Symbolic Data Using a Quantile Representation: Discrete and Continuous Approaches [photo]
Guénaël Cabanes
LIPN- University PARIS 13, France
Advances in Unsupervised Dimensionality Reduction through Topological Clustering and Variable Weighting [slides]
Marc Csernel
INRIA-Rocquencourt, France
Clustering Constrained Symbolic Data [slides] [photo]
Edwin Diday
CEREMADE, Université Paris Dauphine, France. Founder of symbolic data analysis
Symbolic Data Analysis of Complex Data [slides] [photo]
Francisco De Assis Tenorio De Carvalho
A Batch Self-organizing Maps Algorithm for Interval-valued Data [slides] [photo]
Richard Emilion
MAPMO Laboratory, Orléans University, France
PCA, Decision Trees and Classification in Symbolic Contexts [slides] [photo]
Rong Guan
BeiHang University, China
Principal Components Analysis for Normally Distributed Modal Data
Junpeng Guo
Tianjin University, China
A Linear Regression Model For Interval Symbolic Data Considering Inner Points in The Intervals [slides] [photo]
Yves Lechevallie
INRIA-Rocquencourt, France
Graph agregation: extensions of k-SNAP algorithm [slides] [photo]
Wen Long
Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing China
The Style And Structure of The Stock Markets in China: an Application to PCA for Interval Symbolic Data [slides] [photo]
Jie Meng
School of Statistics, Central University of Finance and Economics, Beijing China
Logcontrast PLS Discriminant Model of Compositional Data [slides]
Masahiro Mizuta
Hokkaido University, Japan
Analysis of Symbolic Data with Functional Data Analysis [slides] [photo]
Ndèye Niang
Conservatoire National des Arts et Métiers, France
Hierarchical Mixed Topological Map [slides] [photo]
Monique Noirhomme- Fraiture
University of Namur, Belgium
Analysing hospital patient data with symbolic methods and an R data structure [photo]
Jérôme Pages
Agrocampus, France
Factorial Analysis of Qualitative and Quantitative Data both Mixed and Structured According to Several Hierarchies [slides] [photo]
EM Qannari
Nantes-Atlantic of Veterinary Medicine, Food Science and Engineering National College (ONIRIS), France
A Simple Approach to Analyse Multi-group Datasets: Application to Discrimination and Classification [slides] [photo]
Gilbert Saporta
CNAM, France
A semi-supervised recommender system to predict online job offer performance [slides] [photo]
Arthur Tenenhaus
Supelec, France
Variable Selection for Regularized Generalized Canonical Correlation Analysis [slides] [photo]
Michel Tenenhaus
HEC Paris, France
Regularized Generalized Canonical Correlation Analysis [slides] [photo]
Rosanna Verde
Dipartimento di Studi Europei e Mediterranei - Seconda Università di Napoli, Italy
Histogram Data Analysis Based on Wasserstein Distance [slides] [photo]
Junjie Wu
BeiHang University, Beijing, China
K-Means based Consensus Clustering [slides] [photo]
Abdelkader Zighed
University of Lyon Lumiere, France
New Insights in Topological Learning [photo]

The Workshop will be held in Beihang University. Click here to learn more about Beihang University.