KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/kmitl/1

Browse

Settings

Has files: NoSubject: search.filters.subject.Cluster analysis

Search Results

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Urban rainfall in the Capitals of Brazil: Variability, trend, and wavelet analysis
    (2022-04-01)
    Oliveira-Júnior, José Francisco de
    ;
    Correia Filho, Washington Luiz Félix
    ;
    Monteiro, Lua da Silva
    ;
    Shah, Munawar
    ;
    Hafeez, Amna
    The patterns of urban rainfall in Brazil's capitals are critical, due to population growth and extreme weather. Therefore, the objectives are: i) to identify homogeneous rainfall groups and meteorological systems, ii) to evaluate the trend of the monthly rainfall time series and iii) to apply wavelet analysis to estimate the variance at different frequencies in the rainfall series in the capitals of the Brazil. Monthly rainfall data during 1960–2020 for 27 stations located in the capitals of Brazil were used. The data were flawed, and data imputation (mtsdi package) was applied via Fully Conditional Specification (FCS). Rainfall data were submitted to descriptive, exploratory statistics (boxplot), multivariate analysis (Cluster Analysis - CA) and the Mann-Kendall (MK) test. Seven CA methods (Ward, Single, Complete, Average, McQuity, Median and Centroid) were tested using the cophenetic correlation coefficient (CCC) with a significance level of 5%, the Average method obtained CCC > 0.81 (S). The CA identified three homogeneous regions (G1, G2 and G3) in the capitals of Brazil. The G1 group is formed by the capitals of the Northeast of Brazil (NEB), except for Boa Vista, (North of Brazil - NB). The G2 group is the largest group formed by the capitals of the Midwest (MWB), Southeast (SEB) and South (SB) of Brazil. The G3 group is the smallest group, with the capitals of the NB and some of the NEB. The capitals with the category of significant growth trend were only Porto Alegre and Florianópolis (SB), Vitória (SEB) and Belém (NB). The category of non-significant increase trend prevailed in most capitals of Brazil, with emphasis on the corridor formed between the NB and the Center-South, except for Natal (NEB). The without trend category prevailed in the North, Northeast and Midwest regions of Brazil. Monthly precipitation analyzes for trend detection purposes via Wavelet Analysis showed that ENSO phases are significant in rainfall variability in Brazilian capitals.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Industry cluster using cluster analysis to support industry cluster policy of Thailand
    (2019-06-01)
    Leerojanaprapa, Kanogkan
    ;
    Bhundarak, Komn
    ;
    Sirikasemsuk, Kittiwat
    This research implies clustering into three clusters for five industries by means of k-Means clustering method. Agro-processing, textiles and clothing, petrochemicals and chemicals, electronics and telecommunications equipment and automotive and parts are selected for this study as they are main target promoting industries for Thailand. There were 23,628 firms in this study. From this study, we can identify different patterns of demanded resources in three different groups. One cluster required low level of resourced demands for all variables while the other two clusters required capital and manpower interchangeable between high and medium level. Only the electronics and telecommunications equipment sector showed high to medium demand for all variables. After all firms were divided into three clusters, we were able to define cluster regions by provinces, for particular types of clusters in order to evaluate the potential of each region and also define supporting policy for those firms to meet their demands following the regional economic development strategy.
      2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Data mining for positive customer reaction to advertising in social media
    (2018-01-01)
    Boonjing, Veera
    ;
    Pimchangthong, Daranee
    Social media has become a source of large amounts of data that is extremely useful when data are analyzed properly. Data mining is one of the known techniques to analyze data to find hidden information from a large amount of available data without having prior hypotheses. The objectives of this research were to (1) find the most important factors influencing positive reactions from customers after seeing online advertising in social media, (2) find the most important factors influencing purchasing merchandise that are advertised online, (3) identify customer clusters characteristics that have positive reaction after seeing online advertising in social media, and (4) identify customer clusters characteristics that purchase merchandise after seeing online advertising in social media. The sample size of 370 is collected by questionnaires using convenience sampling method. Data mining with cluster analysis is used to analyze data. The findings indicate the characteristics of “product conscious” and “price conscious” clusters for customer’s reaction and purchasing after seeing online advertising in social media.