Data Clustering with a Relational Push-Pull Model
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Title
Data Clustering with a Relational Push-Pull Model
Author
Anthony , Adam Paul
Advisors
DesJardins , Marie
Program
Computer Science
UMBC Department
Computer Science and Electrical Engineering
Document Type
thesis
Sponsors
University of Maryland , Baltimore County (UMBC)
Keywords
Computer Science (0984) ; relational clustering ; generative model ; statistical relational learning
Date Issued
2007-07-12 ;
Abstract
Relational data clustering is the task of grouping data objects together when both features and relations between objects are present . I present a new generative model for relational data in which relations between objects can have either a binding or separating effect . For example , with a group of students separated into gender clusters , a ``dating ' relation would appear most frequently between the clusters , but a ``roommate ' relation would appear more often within clusters . In visualizing these relations , one can imagine that the ``dating ' relation effectively pushes clusters apart , while the ``roommate ' relation pulls clusters into tighter formations . I use simulated annealing to search for optimal values of the unknown model parameters , where the objective function is a Bayesian score derived from the generative model . Specifically , I show that an assumption that relations should most frequently appear within clusters can lead to poor performance , using experiments with artificial data and two real-world data sets : a Hollywood actor database and an ecological food web . The experiments show that push-type relations do exist , and therefore the tendency of relations to pull clusters together cannot be assumed in general .
Identifier
1020
Format
application:pdf
Language
en
Collection
UMBC Theses and Dissertations
Rights Statement
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu.
Source
umi-umbc-1020.pdf
Access Rights
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan through a local library, pending author/copyright holder's permission.
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