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  • Produktbild: Advances in Data Science and Classification
  • Produktbild: Advances in Data Science and Classification

Advances in Data Science and Classification Proceedings of the 6th Conference of the International Federation of Classification Societies (IFCS-98) Università "La Sapienza", Rome, 21-24 July, 1998

Aus der Reihe Studies in Classification, Data Analysis, and Knowledge Organization

Fr. 137.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.07.1998

Herausgeber

Alfredo Rizzi + weitere

Verlag

Springer Berlin

Seitenzahl

682

Maße (L/B/H)

23.5/15.5/3.7 cm

Gewicht

1042 g

Auflage

Softcover reprint of the original 1st ed. 1998

Sprache

Englisch

ISBN

978-3-540-64641-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.07.1998

Herausgeber

Verlag

Springer Berlin

Seitenzahl

682

Maße (L/B/H)

23.5/15.5/3.7 cm

Gewicht

1042 g

Auflage

Softcover reprint of the original 1st ed. 1998

Sprache

Englisch

ISBN

978-3-540-64641-9

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Advances in Data Science and Classification
  • Produktbild: Advances in Data Science and Classification
  • Methodologies in Classification: Clustering and Classification: J.D. Carroll, A. Chaturvedi: K-Midranges Clustering.- A. Cerioli: A New Method for Detecting Influential Observations in Nonhierarchical Cluster Analysis.- J.C. Gower, G.J.S.Ross: Non-Probabilistic Classification.- A. Hardy, P. Andre: An Investigation of Nine Procedures for Detecting the Structure in a Data Set.- C. Hennig: Clustering and Outlier Identification: Fixed Point Cluster Analysis.- S. Korenjak-Cerne, V. Batagelj: Clustering Large Datasets of Mixed Units.- J.A. Martin-Fernandez, C. Barcelo-Vidal, V. Pawlowsky-Glahn: A Critical Approach to Non-Parametric Classification of Compositional Data.- F. Murtagh, J.L. Stark, M. Berry: Clustering Based on Wavelet Transform: Applications to Point Pattern Clustering and to High-Dimensional Data Analysis.- Comparison and Consensus of Classifications: C. Hayashi, K. Yamaoka: Beyond Simpon`s Paradox: One Problem in Data Science.- F.J. Lapointe: For Consensus (With Branch Lengths).- B. Leclerc: Consensus of Classifications: The Case of Trees.- J. L. Thorley, M. Wilkinson, M. Charleston: The Information Content of Consensus Trees.- Fuzzy Clustering and Fuzzy Methods: S. Bodjanova: Compatible and Complementary Fuzzy Partitions.- S. Miyamoto, K. Umayahara: Two Methods of Fuzzy c-Means and Classification Functions.- M. Ryoke, Y. Nakamori, H. Tamura: Dynamic Determination of Mixing Parameters in Fuzzy Clustering.- M. Sato-Ilic, Y. Sato: A Dynamic Additive Fuzzy Clustering Model.- S. Turpin-Dhilly, C. Botte-Lecocq: Application of Fuzzy Mathematical Morphology for Pattern Classification.- Optimization in Classification and Constrained Classification: K.Bachar, I.-C. Lerman: Statistical Conditions for a Linear Complexity for an Algorithm of Hierarchical Classification Under Constraint of Contiguity.- V. Batagelj, A. Ferligoj: Constrained Clustering Problems.- T. Gastaldi, D. Vicari: A Constrained Clusterwise Procedure for Segmentation.- L. Gueguen, R. Vignes, J. Lebbe: Maximal Predictive Clustering with Order Constraint: a Linear and Optimal Algorithm.- I. Lari, M. Maravalle, B. Simeone: A Linear Programming Based Heuristic for a Hard Clustering Problem on Trees.- M. Mizuta: Two Principal Points of Symmetric Distributions.- N, Nicoloyannis, M. Terrenoire, D. Tounissoux: Pertinence for a Classification.- J. Trejos, A. Murillo, E. Piza: Global Stochastic Optimization Techniques Applied to Partitioning.- Probabilistic Modelling Methods in Classification and Pattern Recognition: H. Bensmail, J.J. Meulman: MCMC Inference for Model-Based Cluster Analysis.- L. Györfi, M. Horvath: On the Asymptotic Normality of a Resubstitution Error Estimate.- G. V. Orman: Stochastic Methods for Generative Systems Analysis.- H.J. Vos: Compensatory Rules for Optimal Classification with Mastery Scores.- Other Approaches for Classification: Discrimination, Neural Network, Regression Tree: Discrimination and Classification: V. Bertholet, J.P. Rasson, S. Lissoir: About the Automatic Detection of Training Sets for Multispectral Images Classification.- A.P.Duarte Silva: A 'Leaps and Bounds' Algorithm for Variable Selection in Two Group Discriminant Analysis.- M.-H. Huh, K-S. Yang: Canonical Discriminant Analysis of Multinomial Samples with Applications to Textual Data.- I.C. Lerman, J.F. Pinto da Costa: How to Extract Predictive Binary Attributes from a Categorical One.- L. Lizzani: A Density Distance Based Approach to Projection Pursuit Discriminant Analysis.- A. Montanari, D. G. Calo: Two Group Linear Discrimination Based on