Data Science and Classification

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Batagelj, Vladimir (Επιμελητής έκδοσης), Bock, Hans-Hermann (Επιμελητής έκδοσης), Ferligoj, Anuška (Επιμελητής έκδοσης), Žiberna, Aleš (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.
• Σειρά: Studies in Classification, Data Analysis, and Knowledge Organization,
• Θέματα: Culture > Study and teaching.; Library science.; Data structures (Computer science).; Computers.; Pattern recognition.; Statistics.; Cultural and Media Studies.; Library Science.; Statistical Theory and Methods.; Data Structures, Cryptology and Information Theory.; Information Systems and Communication Service.; Pattern Recognition.; Statistics for Business/Economics/Mathematical Finance/Insurance.

 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 Classes Using 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.