Data Science: Innovative Developments in Data Analysis and Clustering

Description

This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. The book gathers a selection of peer-reviewed contributions presented at the Fifteenth Conference of the International Federation of Classification Societies (IFCS2015), which was hosted by the Alma Mater Studiorum, University of Bologna, from July 5 to 8, 2015. Front Matter....Pages i-xvi Front Matter....Pages 1-1 Missing Data Imputation and Its Effect on the Accuracy of Classification....Pages 3-14 On Coupling Robust Estimation with Regularization for High-Dimensional Data....Pages 15-27 Classification Methods in the Research on the Financial Standing of Construction Enterprises After Bankruptcy in Poland....Pages 29-42 On the Identification of Correlated Differential Features for Supervised Classification of High-Dimensional Data....Pages 43-57 Front Matter....Pages 59-59 T-Sharper Images and T-Level Cuts of Fuzzy Partitions....Pages 61-72 Benchmarking for Clustering Methods Based on Real Data: A Statistical View....Pages 73-82 Representable Hierarchical Clustering Methods for Asymmetric Networks....Pages 83-95 A Median-Based Consensus Rule for Distance Exponent Selection in the Framework of Intelligent and Weighted Minkowski Clustering....Pages 97-110 Finding Prototypes Through a Two-Step Fuzzy Approach....Pages 111-121 Clustering Air Monitoring Stations According to Background and Ambient Pollution Using Hidden Markov Models and Multidimensional Scaling....Pages 123-132 Marked Point Processes for Microarray Data Clustering....Pages 133-147 Social Differentiation of Cultural Taste and Practice in Contemporary Japan: Nonhierarchical Asymmetric Cluster Analysis....Pages 149-159 The Classification and Visualization of Twitter Trending Topics Considering Time Series Variation....Pages 161-173 Handling Missing Data in Observational Clinical Studies Concerning Cardiovascular Risk: An Insight into Critical Aspects....Pages 175-188 Front Matter....Pages 189-189 Prediction Error in Distance-Based Generalized Linear Models....Pages 191-204 An Inflated Model to Account for Large Heterogeneity in Ordinal Data....Pages 205-217 Functional Data Analysis for Optimizing Strategies of Cash-Flow Management....Pages 219-230 The Five Factor Model of Personality and Evaluation of Drug Consumption Risk....Pages 231-242 Correlation Analysis for Multivariate Functional Data....Pages 243-258 Multi-Dimensional Scaling of Sparse Block Diagonal Similarity Matrix....Pages 259-272 Front Matter....Pages 189-189 The Application of Classical and Positional TOPSIS Methods to Assessment Financial Self-sufficiency Levels in Local Government Units....Pages 273-284 A Method for Transforming Ordinal Variables....Pages 285-294 Big Data Scaling Through Metric Mapping: Exploiting the Remarkable Simplicity of Very High Dimensional Spaces Using Correspondence Analysis....Pages 295-306 Comparing Partial Least Squares and Partial Possibilistic Regression Path Modeling to Likert-Type Scales: A Simulation Study....Pages 307-320 Cause-Related Marketing: A Qualitative and Quantitative Analysis on Pinkwashing....Pages 321-332 Predicting the Evolution of a Constrained Network: A Beta Regression Model....Pages 333-342