The Resource Domain-concept mining : an efficient on-demand data mining approach, by Wannapa Kay Mahamaneerat, (electronic resource)

Domain-concept mining : an efficient on-demand data mining approach, by Wannapa Kay Mahamaneerat, (electronic resource)

Label
Domain-concept mining : an efficient on-demand data mining approach
Title
Domain-concept mining
Title remainder
an efficient on-demand data mining approach
Statement of responsibility
by Wannapa Kay Mahamaneerat
Creator
Contributor
Thesis advisor
Subject
Genre
Language
eng
Summary
Traditional brute-force association mining approaches, when applied to large datasets, are thorough but inefficient due to computational complexity. A low global minimum probability threshold can worsen this complexity by producing an overwhelming number of associations; however, a high threshold may not uncover valuable associations, especially from underrepresented groups within the population. Regardless, the uncovered associations are not systematically organized. To solve these problems, novel Domain-Concept Mining (DCM) with Partition Aggregation (DCM-PA) has been developed. DCM organizes data by grouping transactions with common characteristics, such as a certain age group, into "domain-concepts" (dc). DCM granulizes partitioning criteria by pairing each attribute with its values. Criteria may include under-represented groups as well as spatial, temporal, and incremental dimensionalities. Then, a statistical power analysis is utilized to determine if multiple criteria of the same attribute, such as age group 18-24 and 25-34, should be combined to form a broader partition. Doing so maintains the tradeoff between findings with statistical significance and computational resource consumptions, while preserving data organization. Associations can be extracted from each partition independently because a partition contains all of its qualified transactions. Moreover, the partition size proportionally adjusts the global threshold to be more specific and sensitive. After the initial phase is complete, DCM-PA efficiently reuses DCM's associations to compute results from multiple-partition aggregation (union or intersection) using Bayes Theorem and a pipelining technique. DCM-PA offers the flexibility to perform association mining that is expected to uncovering more valuable knowledge through means like trends and comparisons from various dc partitions and their aggregations
Cataloging source
MUU
http://library.link/vocab/creatorDate
1974-
http://library.link/vocab/creatorName
Mahamaneerat, Wannapa Kay
Degree
Ph.D.
Dissertation year
2008.
Granting institution
University of Missouri--Columbia
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorName
Shyu, Chi-Ren
http://library.link/vocab/subjectName
  • Data mining
  • Association rule mining
  • Data structures (Computer science)
  • Database searching
Target audience
specialized
Label
Domain-concept mining : an efficient on-demand data mining approach, by Wannapa Kay Mahamaneerat, (electronic resource)
Instantiates
Publication
Note
  • Title from PDF of title page (University of Missouri--Columbia, viewed on February 24, 2010)
  • The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file
  • Dissertation advisor: Dr. Chi-Ren Shyu
  • Vita
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
609888853
Extent
1 online resource (xvi, 211 pages)
Form of item
electronic
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (some color).
Specific material designation
remote
System control number
(OCoLC)609888853
Label
Domain-concept mining : an efficient on-demand data mining approach, by Wannapa Kay Mahamaneerat, (electronic resource)
Publication
Note
  • Title from PDF of title page (University of Missouri--Columbia, viewed on February 24, 2010)
  • The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file
  • Dissertation advisor: Dr. Chi-Ren Shyu
  • Vita
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
609888853
Extent
1 online resource (xvi, 211 pages)
Form of item
electronic
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (some color).
Specific material designation
remote
System control number
(OCoLC)609888853

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