The scientific program of the conference will include invited sessions and contributed presentations covering a broad range of topics. A special emphasis is laid on research on and development of innovative tools, techniques and strategies that address current challenges in the data analysis process. We solicit contributions from scholars and practitioners on all domains where statistical and data analytical methods are being developed and applied. We welcome contributions from all areas, in particular the following fields.

  • Data Analysis and Statistics
  • Machine Learning and Knowledge Discovery
  • Data Analysis in Marketing
  • Data Analysis in Finance and Economics
  • Data Analysis in Medicine and the Life Sciences
  • Data Analysis in the Social, Behavioral and Health Care Sciences
  • Data Analysis in Interdisciplinary Domains including Archaeology, Astronomy, Education, Engineering, Geosciences, Linguistics, Logistics, Musicology, Natural Sciences

Topics in these areas include but are not restricted to:

Theory and Methods:

  • Classification and Regression-Type Approaches
  • Decision Trees and Forests
  • Dimensionality Reduction
  • Ensemble Methods
  • Feature Space Reduction
  • Frequent Pattern Mining
  • Knowledge Representation and Discovery
  • Latent Variable Models
  • Machine Learning
  • Methods for Analyzing and Representing Social Networks and Other Large Graphs
  • Methods for Personalization and Recommender Systems
  • Methods for Preference and Positioning Analysis
  • Mixture Models
  • Multivariate Statistical Analysis
  • Relational Learning
  • Regulation Network Inference
  • Statistical Natural Language Processing
  • Statistical Validation
  • Time Series, Longitudinal and Panel Data Analysis

 Data Exploration:

  • Big Data Analytics
  • Data Preparation and Normalization
  • Data Sonification
  • Data Visualization
  • Exploratory Data Analysis and Data Mining
  • Text Mining
  • Web Mining

Applications:

  • Banking and Other Financial Institutions
  • Business Intelligence
  • Costumer Relationship Management
  • Credit Risk Management
  • Data Mining in Finance
  • Education studies
  • Image Analysis and Computer Vision
  • Life Sciences, Biostatistics and Bioinformatics
  • Market Risk Management
  • Medical and Health Sciences
  • Natural Sciences and Geography
  • Political Science and Sociology
  • Production, Controlling and Logistics
  • Psychology
  • Subject Indexing and Library Science

ECDA 2014 also incorporates a Workshop on Library and Information Science (LIS 2014), soliciting contributions on the role of classification and data analysis in this domain.

 

A flyer of the Call for Papers can be downloaded here.