Theory and Application of High-dimensional Complex and Symbolic Data Analysis in Economics and Management Science

Theory and Application of High-dimensional Complex and Symbolic Data Analysis
in Economics and Management Science.

Beijing, China October 27-29, 2011

"Complex data" come from different sources, or live in high dimensional spaces. They are based on several kinds of observations described by standard numerical or (and) categorical data contained in several related data tables. Sometimes, it refers to distributed data or structured data as hierarchical data, spatial-temporal data or heterogeneous data.

In practice, "Complex Data" refers to complex objects like images, video, audio or text documents. "Symbolic Data Analysis" is a new paradigm in which theory, tools and practice have shown its ability to extract new knowledge from complex data. All these specificities call for new data analysis strategies.

The aim of this workshop is to present new theoretical results and applications and to support interdisciplinary research creating interactions between people working in different area of research and interesting to new techniques for the Analysis of Complex Data in the Data Mining and Symbolic Data Analysis framework.

SCIENTIFIC
COMMITTEE

Huiwen Wang(Beihang University, Beijing China) - chair
Edwin Diday(University Paris Dauphine, France)
Yves Lechevallier(INRIA-Rocquencourt, France)
Gilbert Saporta(CNAM, Paris, France)
Michel Tenenhaus(HEC, Jouy en Josas, France)

LOCAL ORGANISING
COMMITTEE

Junjie Wu(Beihang University, China) - chair
Rong Guan(Beihang University, China)

ADVISORY
COMMITTEE

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)

INVITED TALKS

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 CENTRO DE INFORMATICA – CIn/UFPE, Brazil	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]

VENUE

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

CONFERENCE PHOTE