rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Optimization-Based Network Analysis with Applications in Clustering and Data Mining
  name:
    name part: Shahinpour, Shahram
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Butenko, Sergiy
    role:
      role term: thesis advisor.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: In this research we develop theoretical foundations and efficient solution methods for two classes of cluster-detection problems from optimization point of view. In particular, the s-club model and the biclique model are considered due to various application areas. An analytical review of the optimization problems is followed by theoretical results and algorithmic solution methods developed in this research. The maximum s-club problem has applications in graph-based data mining and robust network design where high reachability is often considered a critical property. Massive size of real-life instances makes it necessary to devise a scalable solution method for practical purposes. Moreover, lack of heredity property in s-clubs imposes challenges in the design of optimization algorithms. Motivated by these properties, a sufficient condition for checking maximality, by inclusion, of a given s-club is proposed. The sufficient condition can be employed in the design of optimization algorithms to reduce the computational effort. A variable neighborhood search algorithm is proposed for the maximum s-club problem to facilitate the solution of large instances with reasonable computational effort. In addition, a hybrid exact algorithm has been developed for the problem. Inspired by wide usability of bipartite graphs in modeling and data mining, we consider three classes of the maximum biclique problem. Specifically, the maximum edge biclique, the maximum vertex biclique and the maximum balanced biclique problems are considered. Asymptotic lower and upper bounds on the size of these structures in uniform random graphs are developed. These bounds are insightful in understanding the evolution and growth rate of bicliques in large-scale graphs. To overcome the computational difficulty of solving large instances, a scale-reduction technique for the maximum vertex and maximum edge biclique problems, in general graphs, is proposed. The procedure shrinks the underlying network, by c…
  note: by Shahram Shahinpour.
  note: "Major Subject: Industrial Engineering"
  note: Includes vita.
  note: Doctor of Philosophy Texas A & M University 2013 http://hdl.handle.net/1969.1/151256
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Industrial Engineering
  subject:
    topic: S-club
  subject:
    topic: Biclique
  subject:
    topic: Cluster-detection
  subject:
    topic: Clustering
  subject:
    topic: Scale-reduction
  subject:
    topic: Asymptotic bounds
  identifier: http://hdl.handle.net/1969.1/151256
  record info:
    record content source: TXA
    record creation date: 140312
    record change date: 20151206092547.0
    record identifier: in00003462357
    language of cataloging:
      language term: eng