Title page for ETD etd-050407-005925

Document Type thesis

Author Name Yang, Di

Email Address diyang at wpi.edu

URN etd-050407-005925

Title Analysis Guided Visual Exploration of Multivariate Data

Degree MS

Department Computer Science

Advisors
Elke A. Rundensteiner, Advisor
Matthew O. Ward, Advisor
Carolina Ruiz, Reader
Michael A. Gennert , Department Head

Keywords
Visual Analytics
Visual Knowledge Discovery

Date of Presentation/Defense 2007-04-26

Availability unrestricted

Abstract
Visualization systems traditionally focus on graphical representation
of information. They tend not to provide integrated analytical
services that could aid users in tackling complex knowledge discovery
tasks. Users’ exploration in such environments is usually
impeded due to several problems: 1) Valuable information is hard
to discover, when too much data is visualized on the screen. 2) They
have to manage and organize their discoveries off line, because no
systematic discovery management mechanism exists. 3) Their discoveries
based on visual exploration alone may lack accuracy. 4)
They have no convenient access to the important knowledge learned
by other users. To tackle these problems, it has been recognized
that analytical tools must be introduced into visualization systems.
In this paper, we present a novel analysis-guided exploration system,
called the Nugget Management System (NMS). It leverages
the collaborative effort of human comprehensibility and machine
computations to facilitate users’ visual exploration process. Specifically,
NMS first extracts the valuable information (nuggets) hidden
in datasets based on the interests of users. Given that similar
nuggets may be re-discovered by different users, NMS consolidates
the nugget candidate set by clustering based on their semantic similarity.
To solve the problem of inaccurate discoveries, data mining
techniques are applied to refine the nuggets to best represent the
patterns existing in datasets. Lastly, the resulting well-organized
nugget pool is used to guide users’ exploration. To evaluate the effectiveness
of NMS, we integrated NMS into XmdvTool, a freeware
multivariate visualization system. User studies were performed to
compare the users’ efficiency and accuracy of finishing tasks on
real datasets, with and without the help of NMS. Our user studies
confirmed the effectiveness of NMS.
Keywords: Visual Analytics, Visual Knowledg

Files
thesis.pdf

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Document Type	thesis
Author Name	Yang, Di
Email Address	diyang at wpi.edu
URN	etd-050407-005925
Title	Analysis Guided Visual Exploration of Multivariate Data
Degree	MS
Department	Computer Science
Advisors	Elke A. Rundensteiner, Advisor Matthew O. Ward, Advisor Carolina Ruiz, Reader Michael A. Gennert , Department Head
Keywords	Visual Analytics Visual Knowledge Discovery
Date of Presentation/Defense	2007-04-26
Availability	unrestricted
Abstract Visualization systems traditionally focus on graphical representation of information. They tend not to provide integrated analytical services that could aid users in tackling complex knowledge discovery tasks. Users’ exploration in such environments is usually impeded due to several problems: 1) Valuable information is hard to discover, when too much data is visualized on the screen. 2) They have to manage and organize their discoveries off line, because no systematic discovery management mechanism exists. 3) Their discoveries based on visual exploration alone may lack accuracy. 4) They have no convenient access to the important knowledge learned by other users. To tackle these problems, it has been recognized that analytical tools must be introduced into visualization systems. In this paper, we present a novel analysis-guided exploration system, called the Nugget Management System (NMS). It leverages the collaborative effort of human comprehensibility and machine computations to facilitate users’ visual exploration process. Specifically, NMS first extracts the valuable information (nuggets) hidden in datasets based on the interests of users. Given that similar nuggets may be re-discovered by different users, NMS consolidates the nugget candidate set by clustering based on their semantic similarity. To solve the problem of inaccurate discoveries, data mining techniques are applied to refine the nuggets to best represent the patterns existing in datasets. Lastly, the resulting well-organized nugget pool is used to guide users’ exploration. To evaluate the effectiveness of NMS, we integrated NMS into XmdvTool, a freeware multivariate visualization system. User studies were performed to compare the users’ efficiency and accuracy of finishing tasks on real datasets, with and without the help of NMS. Our user studies confirmed the effectiveness of NMS. Keywords: Visual Analytics, Visual Knowledg
Files	thesis.pdf