Data Science and Classification

• Similarity and Dissimilarity.- A Tree-Based Similarity for Evaluating Concept Proximities in an Ontology.- Improved Fréchet Distance for Time Series.- Comparison of Distance Indices Between Partitions.- Design of Dissimilarity Measures: A New Dissimilarity Between Species Distribution Areas.- Dissimilarities for Web Usage Mining.- Properties and Performance of Shape Similarity Measures.- Classification and Clustering.- Hierarchical Clustering for Boxplot Variables.- Evaluation of Allocation Rules Under Some Cost Constraints.- Crisp Partitions Induced by a Fuzzy Set.- Empirical Comparison of a Monothetic Divisive Clustering Method with the Ward and the k-means Clustering Methods.- Model Selection for the Binary Latent Class Model: A Monte Carlo Simulation.- Finding Meaningful and Stable Clusters Using Local Cluster Analysis.- Comparing Optimal Individual and Collective Assessment Procedures.- Network and Graph Analysis.- Some Open Problem Sets for Generalized Blockmodeling.- Spectral Clustering and Multidimensional Scaling: A Unified View.- Analyzing the Structure of U.S. Patents Network.- Identifying and Classifying Social Groups: A Machine Learning Approach.- Analysis of Symbolic Data.- Multidimensional Scaling of Histogram Dissimilarities.- Dependence and Interdependence Analysis for Interval-Valued Variables.- A New Wasserstein Based Distance for the Hierarchical Clustering of Histogram Symbolic Data.- Symbolic Clustering of Large Datasets.- A Dynamic Clustering Method for Mixed Feature-Type Symbolic Data.- General Data Analysis Methods.- Iterated Boosting for Outlier Detection.- Sub-species of Homopus Areolatus? Biplots and Small Class Inference with Analysis of Distance.- Revised Boxplot Based Discretization as the Kernel of Automatic Interpretation of ClassesUsing Numerical Variables.- Data and Web Mining.- Comparison of Two Methods for Detecting and Correcting Systematic Error in High-throughput Screening Data.- kNN Versus SVM in the Collaborative Filtering Framework.- Mining Association Rules in Folksonomies.- Empirical Analysis of Attribute-Aware Recommendation Algorithms with Variable Synthetic Data.- Patterns of Associations in Finite Sets of Items.- Analysis of Music Data.- Generalized N-gram Measures for Melodic Similarity.- Evaluating Different Approaches to Measuring the Similarity of Melodies.- Using MCMC as a Stochastic Optimization Procedure for Musical Time Series.- Local Models in Register Classification by Timbre.- Gene and Microarray Analysis.- Improving the Performance of Principal Components for Classification of Gene Expression Data Through Feature Selection.- A New Efficient Method for Assessing Missing Nucleotides in DNA Sequences in the Framework of a Generic Evolutionary Model.- New Efficient Algorithm for Modeling Partial and Complete Gene Transfer Scenarios.

From the reviews:"This book is a collection of papers presented at the Tenth Conference of the International Federation of Classification Societies. The contributors are primarily statisticians and computer scientists … . The typesetting and page layout are well done, and the graphics are very clear. … The main market for this book would be libraries, and researchers wanting a record of recent advances in statistical learning." (Jeffrey D. Picka, Technometrics, Vol. 49 (3), August, 2007)