• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1057-1068
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• 2003

Results of using principal component analysis prior to cluster analysis are compared with results from applying agglomerative clustering algorithm alone. The retrieval ability of the agglomerative clustering algorithm is improved by using principal components prior to cluster analysis in some situations. On the other hand, the loss in retrieval ability for the agglomerative clustering algorithms decreases, as the number of informative variables increases, where the informative variables are the variables that have distinct information(or, necessary information) compared to other variables.

• Journal of the Korean Graphic Arts Communication Society
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• v.30 no.2
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• pp.13-22
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• 2012

This research attempts to analyze communication effects of print ad having pictorial typography. 150 Questionnaires were distributed to respondents staying Daejeun City and 148 copies were retreated for five days from April 22nd to 26th, 2012. Frequency analysis, factor analysis, Cronbach's alpha for reliability analysis were utilized for data analysis with SPSS 12.0. For testing hypothesis, regression analysis was used. As result of testing hypothesis, 'informative, beneficial, creative, reliable' were partially significant to attitude towards print ad having pictorial typography. That means 'creative' and 'reliable' were insignificant, while 'informative' and 'beneficial' are significant. Variable of the most influencing on attitude towards advertising is 'informative.' 'Informative, beneficial, creative, and reliable' were partially significant to brand attitude, too. That means 'beneficial' and 'creative' were insignificant, while 'informative' and 'reliable' were significant. Variable of the most influencing on brand attitude was 'reliable.' Therefore, to enhance communication effect of print ad having pictorial typography, 'informative' and 'reliable' are most significant variables.

• Asia-Pacific Journal of Business
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• v.8 no.1
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• pp.15-32
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• 2017

This study was designed to examine the antecedents of mobile app usage among smart phone users. An extended TAM, which included the additional factors of perceived enjoyment, perceived informative usefulness and perceived social usefulness, was applied to predict people's intention to use mobile apps. Overall, the hypothesized research model did a fairly good job explaining significant associations between the independent variables and the dependent variable. The findings had showed that perceived social usefulness, perceived enjoyment and attitude were significantly affect intention to use mobile apps. Meanwhile, perceived ease of use (PEOU) and perceived informative usefulness were not significantly effects attitude towards intention to use mobile apps. Therefore, mobile apps developers should develop mobile apps that are easier for the users to seek information. For the information available should be more precise and bringing more benefits to the users.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.89-94
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• 2001

In this study we focus on variable selection in decision tree growing structure. Some of the splitting rules and variable selection algorithms are discussed. We propose a competitive variable selection method based on Kruskal-Wallis test, which is a nonparametric version of ANOVA F-test. Through a Monte Carlo study we note that CART has serious bias in variable selection towards categorical variables having many values, and also QUEST using F-test is not so powerful to select informative variables under heavy tailed distributions.

• Journal of the Korean Society of Costume
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• v.63 no.2
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• pp.1-13
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• 2013

The purpose of this study is to examine how the features of network in the Internet fashion community affect the diffusion of fashion information to members in the online community with other variables (informative features, consumer features). Communities that actively exchange fashion information among their members were selected for the social network analysis and hypothesis verification. As a result, we found that a few information activists influenced most of the information receivers in the network features of fashion communities. Also, we found that the informative features (usefulness, reliability), consumer features (NFC, innovation) as well as the network features (connectivity, power), have a significant influence on the diffusion of Internet fashion information which verified the importance of the network features in the study on the Internet.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3824-3832
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• 2009

Would it be true that cutting off using BLOG in business hour prevents that job performance decreases? Even though it is right, would the worker have different reason of using BLOG according to job characteristic? This is the purpose of this study to search the answers for the questions above. Under the first hypothesis, (factors having the people use BLOG can influence the job satisfaction), independent variable was set to three factors and define as 'Interoperability', 'Informative', 'Amusement' respectively and dependent variable was set to job satisfaction in this study. The result of analyzing this hypothesis was that two factors('Interoperability', 'Informative') haveinfluence on job satisfaction but 'Amusement' factor hadn't any influence on job satisfaction. For another hypothesis, (the factor having the worker use BLOG would have different influence on job satisfaction according to job characteristic), Job characteristic was set to 3 group (fixed/unfixed, individual/co-operational, static/active) in this study and these variables were converted to dummy variable for validating the moderating effect on both variables(independent/dependent). The result of analyzing this hypothesis was that all dummy variables set to 3 groupshadn't any moderating effect on both variables. Because a dummy variable couldn't be contained the job characteristic exactly.

• Communications for Statistical Applications and Methods
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• v.17 no.5
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• pp.689-696
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• 2010

Interval-censored data are commonly found in studies of diseases that progress without symptoms, which require clinical evaluation for detection. Several techniques have been suggested with independent assumption. However, the assumption will not be valid if observations come from clusters. Furthermore, when the cluster size relates to response variables, commonly used methods can bring biased results. For example, in a study on lymphatic filariasis, a parasitic disease where worms make several nests in the infected person's lymphatic vessels and reside until adulthood, the response variable of interest is the nest-extinction times. Since the extinction times of nests are checked by repeated ultrasound examinations, exact extinction times are not observed. Instead, data are composed of two examination points: the last examination time with living worms and the first examination time with dead worms. Furthermore, as Williamson et al. (2008) pointed out, larger nests show a tendency for low clearance rates. This association has been denoted as an informative cluster size. To analyze the relationship between the numbers of nests and interval-censored nest-extinction times, this study proposes a joint model for the relationship between cluster size and clustered interval-censored failure data.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1213-1224
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• 2011

In high dimensionality where the number of variables are excessively larger than observations, it is required to remove the noninformative variables to cluster observations. Most model-based approaches for variable selection have been considered under the assumption of homoscedasticity and their models are mainly estimated by a penalized likelihood method. In this paper, a different approach is proposed to remove the noninformative variables effectively and to cluster based on the modified normal mixture model simultaneously. The validity of the model was provided and an EM algorithm was derived to estimate the parameters. Simulation studies and an experiment using real microarray dataset showed the effectiveness of the proposed method.

• Journal of the Korean Home Economics Association
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• v.41 no.3
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• pp.93-111
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• 2003

This study aimed to examine the water consumption of urban households according to pro-environmental attitude for environmental protection. In contrast to preview studies, this study purposed to include various related independent variables, motive to environmental behavior, in special, in the model, and suggest informative data for research, education and strategies related to environmental protection. The data were from 665 housewives living in five urban areas. For the analysis of data, frequencies, means, percentages, GLM analysis, DMR test and Chi-square test were used. The main results of this study were as follows; 1. The respondents held high pro-environmental attitude that pro-environmental behaviors are important to protect environment. The pro-environmental attitude among the respondents were statistically different from the independent variables : spouse's occupation, living area, help of housekeeper, knowledge about environmental protection, convenience to check water consumption, and perception of voluntary conservative behavior among neighborhood 2. There were great difference on water consumption among respondents. The significant independent variables to have effects on water consumption were different between water consumption per person and higher/lower average water consumption. The relationships of pro-environmental attitude and motive to pro-environmental behavior with two water consumption as dependent variables were unique. Also, living areas and knowledge about environment protection were consistently important to explain the difference of water consumption.

• Bulletin of the Korean Chemical Society
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• v.33 no.5
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• pp.1527-1535
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• 2012

Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms.