Data Science, Learning by Latent Structures, and Knowledge Discovery [Paperback]

This volume comprises papers dedicated to data science and the extraction of knowledge from many types of data: structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering and pattern recognition methods; strategies for modeling complex data and mining large data sets; applications of advanced methods in specific domains of practice. The contributions offer interesting applications to various disciplines such as psychology, biology, medical and health sciences; economics, marketing, banking and finance; engineering; geography and geology; archeology, sociology, educational sciences, linguistics and musicology; library science. The book contains the selected and peer-reviewed papers presented during the European Conference on Data Analysis (ECDA 2013) which was jointly held by the German Classification Society (GfKl) and the French-speaking Classification Society (SFC) in July 2013 at the University of Luxembourg.

Part I Invited Papers: Modernising Official Statistics - A Complex Challenge.- A New Supervised Classification of Credit Approval Data via the Hybridized RBF Neural Network Model Using Information Complexity.- Finding the Number of Disparate Clusters with Background Contamination.- Clustering of Solar Irradiance.- Part II Data Science and Clustering: Factor Analysis of Local Formalism.- Recent progress in Complex Network Analysis  Models.- Recent Progress in Complex Network Analysis  Results.-  Similarity Measures of Concept Lattices.- Flow-Based Dissimilarities: Shortest Path, Commute Time, Max-Flow and Free Energy.- Resampling Techniques in Cluster Analysis  Is Subsampling Better Than Bootstrapping?.- On-Line Clustering of Functional Boxplots for Monitoring Multiple Streaming Time Series.- Smooth Tests of Fit for Gaussian Mixtures.- Part III Machine Learning and Knowledge Discovery: P2P RVM for DistributelÓ$