Game Theoretical Approach for Clustering and Its Application in Big Data Analytics

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Material Information

Title:
Game Theoretical Approach for Clustering and Its Application in Big Data Analytics
Physical Description:
1 online resource (140 p.)
Language:
english
Creator:
Sun, Baohua
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
Publication Date:

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Electrical and Computer Engineering
Committee Chair:
WU,DAPENG
Committee Co-Chair:
LI,XIAOLIN
Committee Members:
MCNAIR,JANISE Y
CHEN,SHIGANG

Subjects

Subjects / Keywords:
big -- clustering -- data -- game -- graph -- gtac -- soq -- subclass -- theoretical
Electrical and Computer Engineering -- Dissertations, Academic -- UF
Genre:
Electrical and Computer Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract:
Big data analytics, which studies large amounts of data of various types to disclose hidden patterns, is attracting more and more attention in both academic and industrial areas. One of the most significant problem of big data analytics is the task of clustering analysis, which is to group similar objects in the same group which we call cluster or community, and separate dissimilar objects into different groups. In this work, we propose two schemes to address clustering problem. The first one takes the bio-inspired approach of Self-Organizing-Queue to address the problem of graph clustering, where the similarity matrix and the number of clusters are given. The second one takes the perspective of game theory to attack the community detection problem, where clustering is to be done only through the adjacency list information. We derive a family of Game Theoretical Approach for Clustering algorithms according to the objective function of the game. Experimental results show the superiority of our proposed schemes and its potential advantages in application of big data analytics.
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Baohua Sun.
Thesis:
Thesis (Ph.D.)--University of Florida, 2013.
Local:
Adviser: WU,DAPENG.
Local:
Co-adviser: LI,XIAOLIN.
Electronic Access:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2015-12-31

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2013
System ID:
UFE0046268:00001