Data Science and Classification

By: Vladimir Batagelj (Editor), Hans-Hermann Bock (Editor), AnuÅ¡ka Ferligoj (Editor)

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Paperback | 5 July 2006

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372 Pages

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22.86 x 15.24 x 1.91

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Data Science and Classification provides new methodological developments in data analysis and classification. The broad and comprehensive coverage includes the measurement of similarity and dissimilarity, methods for classification and clustering, network and graph analyses, analysis of symbolic data, and web mining. Beyond structural and theoretical results, the book offers application advice for a variety of problems, in medicine, microarray analysis, social network structures, and music.

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"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)

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The 10th IFCS conference - a jubilee
A tree-based similarity for evaluating concept proximities in an ontology	p. 3
Improved Frechet distance for time series	p. 13
Comparison of distance indices between partitions	p. 21
Design of dissimilarity measures : a new dissimilarity between species distribution areas	p. 29
Dissimilarities for Web usage mining	p. 39
Properties and performance of shape similarity measures	p. 47
Hierarchical clustering for boxplot variables	p. 59
Evaluation of allocation rules under some cost constraints	p. 67
Crisp partitions induced by a fuzzy set	p. 75
Empirical comparison of a monothetic divisive clustering method with the ward and the k-means clustering methods	p. 83
Model selection for the binary latent class model : a Monte Carlo simulation	p. 91
Finding meaningful and stable clusters using local cluster analysis	p. 101
Comparing optimal individual and collective assessment procedures	p. 109
Some open problem sets for generalized blockmodeling	p. 119
Spectral clustering and multidimensional scaling : a unified view	p. 131
Analyzing the structure of U.S. patents network	p. 141
Identifying and classifying social groups : a machine learning approach	p. 149
Multidimensional scaling of histogram dissimilarities	p. 161
Dependence and interdependence analysis for interval-valued variables	p. 171
A new Wasserstein based distance for the hierarchical clustering of histogram symbolic data	p. 185
Symbolic clustering of large datasets	p. 193
A dynamic clustering method for mixed feature-type symbolic data	p. 203
Iterated boosting for outlier detection	p. 213
Sub-species of Homopus Areolatus? : biplots and small class inference with analysis of distance	p. 221
Revised boxplot based discretization as the kernel of automatic interpretation of classes using numerical variables	p. 229
Comparison of two methods for detecting and correcting systematic errors in high-throughput screening data	p. 241
kNN versus SVM in the collaborative filtering framework	p. 251
Mining association rules in folksonomies	p. 261
Empirical analysis of attribute-aware recommendation algorithms with variable synthetic data	p. 271
Patterns of associations in finite sets of items	p. 279
Generalized N-gram measures for melodic similarity	p. 289
Evaluating different approaches to measuring the similarity of melodies	p. 299
Using MCMC as a stochastic optimization procedure for musical time series	p. 307
Local models in register classification by timbre	p. 315
Improving the performance of principal components for classification of gene expression data through feature selection	p. 325
A new efficient method for assessing missing nucleotides in DNA sequences in the framework of a generic evolutionary model	p. 333
New efficient algorithm for modeling partial and complete gene transfer scenarios	p. 341
Table of Contents provided by Blackwell. All Rights Reserved.

Data Science and Classification

At a Glance

Paperback

Industry Reviews

Shipping

How to return your order

Defective items

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