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UBC Theses and Dissertations
UBC Theses and Dissertations
Finding strong, common and discriminating characteristics of clusters from thematic maps Yu, YiQing
Abstract
The goal of the thesis is to discover knowledge on large spatial databases. Specifically, it discusses the problem of extracting patterns and characteristics of clusters from thematic maps. For instance, a characteristic of an expensive housing cluster may be that the average household income is over 100,000 dollars. Three key issues are addressed in this thesis. The first issue is how to measure the interest/utility values of characteristics. In order to accommodate different kinds of thematic maps, two measures are proposed and analysed: one based on entropy, and the other on standard deviation. Both measures satisfy all the desirable properties, and work effectively in practice. The second issue is how to extract patterns from multiple clusters. Two pattern extraction operations are defined. The common() operation is able to find the common characteristics among multiple clusters. The different() operations is capable of discovering the characteristics which distinguish one cluster from another. The third issue is how to compute characteristics utility measures and pattern extraction operations efficiently. Four different methods are proposed and evaluated for the computation of utility measures. Complexity and experimental results indicates that a technique based on isothetic rectangle intersections is the most efficient, outperforming all the other techniques such as a technique based on R-tree technique. For the problem of how to extract patterns of multiple clusters efficiently. Two different methods for pattern extraction are evaluated. The technique based on isothetic rectangle intersections again outperforms the technique based on R-tree, and can extract patterns from hundreds of thematic maps in seconds of CPU time.
Item Metadata
Title |
Finding strong, common and discriminating characteristics of clusters from thematic maps
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
1996
|
Description |
The goal of the thesis is to discover knowledge on large spatial databases. Specifically, it
discusses the problem of extracting patterns and characteristics of clusters from thematic
maps. For instance, a characteristic of an expensive housing cluster may be that the average
household income is over 100,000 dollars. Three key issues are addressed in this thesis. The
first issue is how to measure the interest/utility values of characteristics. In order to
accommodate different kinds of thematic maps, two measures are proposed and analysed: one
based on entropy, and the other on standard deviation. Both measures satisfy all the desirable
properties, and work effectively in practice. The second issue is how to extract patterns from
multiple clusters. Two pattern extraction operations are defined. The common() operation is
able to find the common characteristics among multiple clusters. The different() operations is
capable of discovering the characteristics which distinguish one cluster from another. The third
issue is how to compute characteristics utility measures and pattern extraction operations
efficiently. Four different methods are proposed and evaluated for the computation of utility
measures. Complexity and experimental results indicates that a technique based on isothetic
rectangle intersections is the most efficient, outperforming all the other techniques such as a
technique based on R-tree technique. For the problem of how to extract patterns of multiple
clusters efficiently. Two different methods for pattern extraction are evaluated. The technique
based on isothetic rectangle intersections again outperforms the technique based on R-tree, and
can extract patterns from hundreds of thematic maps in seconds of CPU time.
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Extent |
3084039 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-02-14
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0051229
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
1996-11
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.