• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.439-454
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• 2013

This study presents a Bayesian multiple change-point detection approach to segment and classify the observations that no longer come from an initial population after a certain time. Inferences are based on the multiple change-points in a sequence of random variables where the probability distribution changes. Bayesian multiple change-point estimation is classifies each observation into a segment. We use a truncated Poisson distribution for the number of change-points and conjugate prior for the exponential family distributions. The Bayesian method can lead the unsupervised classification of discrete, continuous variables and multivariate vectors based on latent class models; therefore, the solution for change-points corresponds to the stochastic partitions of observed data. We demonstrate segmentation with real data.

• The Research Journal of the Costume Culture
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• v.19 no.2
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• pp.360-375
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• 2011

The fashion market focuses on consumers and maximizes consumers' satisfaction. The fashion market has been segmented to better satisfy the variety of consumer group. Although market segmentation has been studied, efficiency and effectiveness of market segmentation continuously bring problems. Also, problems of prediction about real consumer behavior, and efficiency and effectiveness of standards are pointed out. The purpose of this study is to determine the most important variables for dividing fashion product buyers. This study was designed as qualitative study and in-depth interview was conducted. The in-depth interview was conducted with five experts in fashion intelligence agency. In-depth interview was completed by an analytic induction and an investigator triangulation. Questions were about characteristics, demographic characteristics, important factors and fashion buying relationship, and interests of current clothing shoppers. The results of qualitative research demonstrated that clothing shoppers, with their valuable consumption and selective buying behaviors, seek differentiated products. They also long for high quality apparel for its price, because of their valuable consumption and price centered tendency. They illustrated active sides, such as enthusiastic information searching and emotional or experiential consumption, rather than attitudinal sides. The variables for dividing fashion product buyers included: "innovative seeking", "symbolic seeking", "personalized seeking", "quality-seeking", "selective seeking", "price-seeking", "utility-seeking", "hedonic seeking", "sensitive seeking", "brand-seeking", "digital seeking", "information-seeking", and "eco-seeking".

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.4
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• pp.161-184
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• 2011

As movie demands become more and more diversified, it is necessary for movie related firms to segment a whole heterogeneous market into a number of small homogeneous markets in order to identify the specific needs of consumer groups. Relevant market segmentation helps them to develop valuable offer to target segments through effective marketing planning. In this article, we introduce various segmentation methods and compare their advantages and disadvantages. In particular, we analyze "2009~2010 consumer survey data of Korean Film Industry" by using Latent Class Analysis(LCA), a statistical segmentation method which identifies exclusive set of latent classes based on consumers' responses to an observed categorical and numerical variables. It is applied PROC LCA, a new SAS procedure for conducting LCA and finally get the result of 11 distinctive clusters showing unique characteristics on their buying behaviors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3204-3220
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• 2021

We perform the task of video object segmentation by incorporating a conditional random field (CRF) and convolutional neural networks (CNNs). Most methods employ a CRF to refine a coarse output from fully convolutional networks. Others treat the inference process of the CRF as a recurrent neural network and then combine CNNs and the CRF into an end-to-end model for video object segmentation. In contrast to these methods, we propose a novel higher-order CRF model to solve the problem of video object segmentation. Specifically, we use CNNs to establish a higher-order dependence among pixels, and this dependence can provide critical global information for a segmentation model to enhance the global consistency of segmentation. In general, the optimization of the higher-order energy is extremely difficult. To make the problem tractable, we decompose the higher-order energy into two parts by utilizing auxiliary variables and then solve it by using an iterative process. We conduct quantitative and qualitative analyses on multiple datasets, and the proposed method achieves competitive results.

• Asia Marketing Journal
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• v.17 no.1
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• pp.1-26
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• 2015

In the 21^st century, green consumer behavior, playing one of the core roles of sustainability, is still an important issue to green-related stakeholders. Because one of the major objectives of green-consumer research is an improvement of behaviors aligned with greening, this paper revisited socio-demographic variables and shed light on segmenting and profiling green consumers based on their connectedness between socio-demographic variables and green behaviors. Using correlations, factor analysis, analysis of variance, k-means cluster analysis and χ²-tests, this paper shows that socio-demographic variables differentially impact green-consumer behaviors. In order to profile green consumers, this paper additionally attempts to segment green-consumer groups. The results also coincide with former findings that socio-demographic variables relate significantly with segmented green-consumer group behaviors. General findings are summarized as: 1) older people used green practices more strongly than younger people, 2) females demonstrated better energy-saving and recycling practices compared to males, 3) marital status also significantly influenced green-related behaviors, 4) subjective social class had a significant influence on green-related behaviors, 5) education level and income, however, weakly influenced or showed no impact on green-related behaviors, and 6) a green consumer was classified as an 'active green consumer,' 'utilitarian green consumer,' or 'inactivated green consumer.' The utilitarian green consumer group distinctively behaved more strongly in energy-saving and recycling practices compared to the inactivated green consumer group, whereas active green consumers behaved more strongly on the whole, when compared to those in the inactivated green consumer group.

• Environmental and Resource Economics Review
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• v.16 no.3
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• pp.701-721
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• 2007

A key issue in the development and application of stated preference nonmarket valuation is the incorporation of unobserved heterogeneity in utility models. Two approaches to this task have dominated. The first is to include individual-specific characteristics into the estimated indirect utility functions. These characteristics are usually socioeconomic or demographic variables. The second employs generalized models such as random parameter logit or probit models to allow model parameters to vary across individuals. This paper examines a third approach: the inclusion of psychological or 'latent' variables such as general attitudes and behaviour-specific attitudes to account for heterogeneity in models of stated preferences. Attitudinal indicators are used as explanatory variables and as segmentation criteria in a choice modelling application. Results show that both the model significance and parameter estimates are influenced by the inclusion of the latent variables, and that attitudinal variables are significant factors for WTP estimates.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.11
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• pp.1605-1614
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• 2002

The purpose of this study was to identify the consumers who use the level of price as the indicator of the product quality. In order to implement the purpose of this study, Jeans market had been segmented by the mixture regression model, and price response function was calibrated for each segment. Based on the types of price response function, segments were allocated into one of two groups; the group using the level of price as the quality indicator or the group not using the level of price as that. Then, characteristics of both groups were compared in terms of product attributes and demographic variables. Data were co]looted from the sample of the 23o undergraduate and graduate students in Seoul. For the data analysis, mixture regression model, conjoint analysis, and t-test were used. As a result, jeans market was divided into 5 segments. Segment 1,2,3 were allocated into the group not using the level of price as the quality indicator while segment 4,5 were done into the other group. Significant differences existed between two groups in product attributes, not in demographic variables. Mixture model and conjoint analysis were proved to be an effective set of tools in market segmentation.

• Journal of the Korean Institute of Landscape Architecture
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• v.20 no.2
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• pp.18-26
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• 1992

The purpose of this study is to suggest a method for identifying target markets of potential urban park users by their sociodemographic variables. Data was classified into(ⅰ) users vs. nonusers ; (ⅱ) of chosen three urban parks ; or(ⅲ) users of each urban park then analyzed by discriminant analysis. The results showed that linear combination of selected sociodemographic variables could be used for identifying target markets in some cases. In general, season and sex were the most powerful discriminant variables. But the other cases were not satisfactory. The weak points of this study due to adapting secondary data for analysis were discussed.

• Korean Business Review
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• v.14
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• pp.129-148
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• 2001

The purpose of this study is to survey the LOV(List Of Values) composed with the consumer's psychographic variables and is to segment consumer's value system through the LOV. Referring to the previous studies such as Rokeach's RVS, Kahke's LOV, Razzaque's Asian values, and author's Korean values, this study conducted researches of consumer's value segmentation. The result of this study finds that the Korean consumer's value system is categorized into (1) value of influence, (2) value of mammonism, (3) value of latest, (4) value of venture, (5) value of salvation, (6) value of sociality, (7) value of curiosity, (8) spirituality, and (9) value of conservativeness in order. This results will help contribute toward segmentation of Korean consumer's value. Another result of this study shows by way of ANOVA(analysis of variance) that the Korean consumer's value dimensions are closely related to demographic variables: sex, marital status, education, occupation, total families income by monthly, family structure, and age.

• Korean Business Review
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• v.11
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• pp.149-171
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• 1998

The most important thing in the Tourist Market Segmentation is to find descriptive variables which can describe the changes of tourist demand properly. There are many descriptive variables. Among them, vital statistical variables were proved to be effective. The strongest variable but which was studied much less is the Family Life Cycle. This study will focus on the relation between Family Life Cycle and Travel Behavior of Destination Choice. In this study, I will verify the validity of Family Life Cycle as a descriptive variable of Tourist Market Segmentation, and try to find the meaningful variable at each steps. Therefore, The purpose of this study is to explain the relation between Family Life Cycle and Travel Behavior of Destination Choice, to verify the validity of Family Life Cycle as descriptive variable and to find the strategy to respond to the increase in quantity and diversity of quality of Tourist Market. The studies on the Family Life Cycle should be updated continuously according to the change of family structure and it should be understood as standard for Tourist Market Segmentation in the public and private sphere.