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Clusters, Orders, and Trees: Methods and Applications

In Honor of Boris Mirkin's 70th Birthday

  • Book
  • © 2014

Overview

Editors:
  1. Fuad Aleskerov

    1. Department of Higher Mathematics, National Research University Higher School of Economics, Moscow, Russia

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    You can also search for this editor in PubMed   Google Scholar

  2. Boris Goldengorin

    1. Department of Operations, University of Groningen, The Netherlands

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    You can also search for this editor in PubMed   Google Scholar

  3. Panos M. Pardalos

    1. Department of Industrial and Systems Eng, University of Florida, Gainesville, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Contains new models and algorithms for knowledge discoveries
  • Features new tools for developing practical algorithms for solving problems in data analysis
  • Opens a new direction in addressing difficult questions in data analysis for either well-known tools (like K-means) or recently developed advanced tools
  • Includes supplementary material: sn.pub/extras

Part of the book series: Springer Optimization and Its Applications (SOIA, volume 92)

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Table of contents (23 chapters)

  1. Front Matter

    Pages i-xi
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  2. Three and One Questions to Dr. B. Mirkin About Complexity Statistics

    • Igor Mandel
    Pages 1-9

  3. Classification and Cluster

    1. Front Matter

      Pages 11-11
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    2. A Polynomial Algorithm for a Class of 0–1 Fractional Programming Problems Involving Composite Functions, with an Application to Additive Clustering

      • Pierre Hansen, Christophe Meyer
      Pages 13-50

    3. Experiments with a Non-convex Variance-Based Clustering Criterion

      • Rodrigo F. Toso, Evgeny V. Bauman, Casimir A. Kulikowski, Ilya B. Muchnik
      Pages 51-62

    4. Strategy-Proof Location Functions on Finite Graphs

      • F. R. McMorris, Henry Martyn Mulder, Fred S. Roberts
      Pages 63-75

    5. A Pseudo-Boolean Approach to the Market Graph Analysis by Means of the p-Median Model

      • Boris Goldengorin, Anton Kocheturov, Panos M. Pardalos
      Pages 77-89

    6. Clustering as an Approach to 3D Reconstruction Problem

      • Sergey Arkhangelskiy, Ilya Muchnik
      Pages 91-102

    7. Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting

      • Renato Cordeiro de Amorim, Boris Mirkin
      Pages 103-117

    8. High-Dimensional Data Classification

      • Vijay Pappu, Panos M. Pardalos
      Pages 119-150

    9. Algorithm FRiS-TDR for Generalized Classification of the Labeled, Semi-labeled and Unlabeled Datasets

      • I. A. Borisova, N. G. Zagoruiko
      Pages 151-165

    10. From Separating to Proximal Plane Classifiers: A Review

      • Maria Brigida Ferraro, Mario Rosario Guarracino
      Pages 167-180

    11. A Note on the Effectiveness of the Least Squares Consensus Clustering

      • Boris Mirkin, Andrey Shestakov
      Pages 181-185

  4. Order and Tree

    1. Front Matter

      Pages 187-187
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    2. Single or Multiple Consensus for Linear Orders

      • Alain GuĂ©noche
      Pages 189-199

    3. Choice Functions on Tree Quasi-Orders

      • F. R. McMorris, R. C. Powers
      Pages 201-210

    4. Weak Hierarchies: A Central Clustering Structure

      • Patrice Bertrand, Jean Diatta
      Pages 211-230

    5. Some Observations on Oligarchies, Internal Direct Sums, and Lattice Congruences

      • Melvin F. Janowitz
      Pages 231-247

    6. Thinking Ultrametrically, Thinking p-Adically

      • Fionn Murtagh
      Pages 249-272

    7. A New Algorithm for Inferring Hybridization Events Based on the Detection of Horizontal Gene Transfers

      • Vladimir Makarenkov, Alix Boc, Pierre Legendre
      Pages 273-293
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Keywords

About this book

The volume is dedicated to Boris Mirkin on the occasion of his 70th birthday. In addition to his startling PhD results in abstract automata theory, Mirkin’s ground breaking contributions in various fields of decision making and data analysis have marked the fourth quarter of the 20th century and beyond. Mirkin has done pioneering work in group choice, clustering, data mining and knowledge discovery aimed at finding and describing non-trivial or hidden structures—first of all, clusters, orderings and hierarchies—in multivariate and/or network data.

This volume contains a collection of papers reflecting recent developments rooted in Mirkin’s fundamental contribution to the state-of-the-art in group choice, ordering, clustering, data mining and knowledge discovery. Researchers, students and software engineers will benefit from new knowledge discovery techniques and application directions.

Editors and Affiliations

  • Department of Higher Mathematics, National Research University Higher School of Economics, Moscow, Russia

    Fuad Aleskerov

  • Department of Operations, University of Groningen, The Netherlands

    Boris Goldengorin

  • Department of Industrial and Systems Eng, University of Florida, Gainesville, USA

    Panos M. Pardalos

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