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ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
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Scopus
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CNKI
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etc
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Acceptance Rate:
19%
APC:
500 USD
Average Days to Accept:
135 days
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Impact Factor 2022: 1.0
3.1
2022
CiteScore
49th percentile
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2024-03-28
Vol. 15, No. 3 has been published online!
2024-02-26
The papers published in Vol. 15, Nos. 1&2 have been registered with Crossref.
2024-02-26
Vol. 15, No. 2 has been published online!
Home
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2018
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Volume 9, No. 2, May 2018
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Segmentation of Domestic Tourist in Thailand by Combining Attribute Weight with Clustering Algorithm
Prapassorn Hayamin and Anongnart Srivihok
Department of Computer Science, Faculty of Science, Kasetsart University, Bangkok, Thailand
Abstract
—The tourism industry is growing up and competing relatively high. In Thailand, tourism is one of the main industries that can generate a large amount of domestic turnover rate. And tourist information in Thailand is stored in large quantities. It is difficult to understand the needs of tourists. Therefore, this study presents segmentation of domestic tourist in Thailand by combining attribute weight with clustering algorithm. The study used two step algorithms, in the first step, Self-Organizing Maps (SOM) was used to determine the optimum number of clusters which an input parameter to K-Means and Fuzzy C-Means. Then, using SOM, K-Means and Fuzzy C-Means algorithms combine with feature weighting techniques based on Correlation Coefficient (CC), Information Gain Ratio (IGR), Gini Index and Principal Components Analysis (PCA) for clustering the tourists clusters. The quality of cluster was measured by Davies Bouldin Index (DB), Root Mean Square Standard Deviation (RMSSTD) and R Square (RS). The results of this study might be used for tourism management and entrepreneur tour and travel can be used for decision making and business planning.
Index Terms
—domestic tourism, clustering, attribute weight, SOM, K-means, fuzzy C-means
Cite: Prapassorn Hayamin and Anongnart Srivihok, "Segmentation of Domestic Tourist in Thailand by Combining Attribute Weight with Clustering Algorithm," Vol. 9, No. 2, pp. 39-44, May 2018. doi: 10.12720/jait.9.2.39-44
3-F037
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