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  1. Home
  2. 1. Schools / Colleges
  3. School of Science and Technology (ST)
  4. ST: Research Reports
  5. Socio-Economic Household Data Analysis Using the Clustering and Association Technique for Data Mining
 
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Socio-Economic Household Data Analysis Using the Clustering and Association Technique for Data Mining

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Author(s)
ชฎารัตน์ พิพัฒนนันท์ 
สิริธร เจริญรัตน์ 
Mongkolsripattana, Sasithorn 
Other Contributor(s)
University of the Thai Chamber of Commerce. School of Science and Technology
Publisher(s)
University of the Thai Chamber of Commerce
Date Issued
2013
Resource Type
Text::Report::Research report
Language
eng
Abstract
In this research, we studies and analyses the data from the Household Socio-Economic Survey 2009 of the Office of National Statistics, Thailand. We use K-means algorithm to cluster the expenditure of the population. By using DB Index and SD Validity, Index, we found that the appropriate number of clusters is three clusters. Then we use association rule technique to determine the relationship between variables. The results show that the association rules are similar among clusters. For example, the average monthly household income is related to the average monthly household cost. Besides, household size is related to number of earners per household. Furthermore, the monthly tobacco cost for a household is associated with a number of members who are entitled to reimbursement for medical expenses.
Sponsorship
Research Support Office, UTCC
Subject(s)
Information and Communication Technology Management
Degree Grantor
University of the Thai Chamber of Commerce
Access Rights
Open access
Rights
This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
Rights Holder
University of the Thai Chamber of Commerce
Bibliographic Citation
Chadarat Phipathananunth, Sirithorn Jalearnrat, Sasithorn Mongkolsripattana (2013) Socio-Economic Household Data Analysis Using the Clustering and Association Technique for Data Mining.
URI
https://hdl.handle.net/20.500.14437/549
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Acquisition Date
Sep 17, 2026
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