• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.319-320
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• 2015

본 연구는 빅데이터를 활용해서 유권자의 정치성향을 파악할 수 있는 방안을 세 가지 관점에서 제시하였다. 첫째, 군집분석은 유권자의 기본성향을 파악할 수 있는 방법으로 각 정당은 유권자의 데이터베이스를 확보해야 한다. 둘째, 회귀분석은 독립변수가 종속변수에 어떤 영향을 끼치는가를 분석한 것으로 유권자들의 필요에 따른 정책을 세우는데 필요하다. 셋째, 연관성 분석은 특정 사물에 대한 선호도를 파악하여 유권자의 정치성향을 유추할 수 있는 방안을 말한다.

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2002.10a
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• pp.245-246
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• 2002

우리나라에 서식하는 전복류는 참전복(Haziotis discus hannai), 까막전복(H, disus), 말전복(H. gigantea), 시볼트전복(H. siebolidi)이 주로 서식하는 것으로 알려져 있으며, 원시복족류인 전복류는 잘 발달된 치설(radula)로 grazing 하는 섭식형태를 가지는 초식동물(herbivores)이다. 초식동물은 해중림 지역에서 다양한 생물군집이 직ㆍ간접적으로 관련되어 생태계를 유지하며(Lobban and harrison, 1994), 이들의 초식작용은 생태계에서 다양한 군집을 구조화하는 중요한 과정이다(John et al., 1992) (중략)

• Journal of Global Scholars of Marketing Science
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• v.17 no.3
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• pp.1-19
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• 2007

1. Purpose Rural tourism is made by individuals with different characteristics, needs and wants. It is important to have information on the characteristics and preferences of the consumers of the different types of existing rural accommodation. The stud aims to identify the determinants of consumer preference by type of accommodations. 2. Methodology 2.1 Sample Data were collected from 1000 people by telephone survey with three-stage stratified random sampling in seven metropolitan areas in Korea. Respondents were chosen by sampling internal on telephone book published in 2006. We surveyed from four to ten-thirty 0'clock afternoon so as to systematic sampling considering respondents' life cycle. 2.2 Two-step cluster Analysis Our study is accomplished through the use of a two-step cluster method to classify the accommodation in a reduced number of groups, so that each group constitutes a type. This method had been suggested as appropriate in clustering large data sets with mixed attributes. The method is based on a distance measure that enables data with both continuous and categorical attributes to be clustered. This is derived from a probabilistic model in which the distance between two clusters in equivalent to the decrease in log-likelihood function as a result of merging. 2.3 Multinomial Logit Analysis The estimation of a Multionmial Logit model determines the characteristics of tourist who is most likely to opt for each type of accommodation. The Multinomial Logit model constitutes an appropriate framework to explore and explain choice process where the choice set consists of more than two alternatives. Due to its ease and quick estimation of parameters, the Multinomial Logit model has been used for many empirical studies of choice in tourism. 3. Findings The auto-clustering algorithm indicated that a five-cluster solution was the best model, because it minimized the BIC value and the change in them between adjacent numbers of clusters. The accommodation establishments can be classified into five types: Traditional House, Typical Farmhouse, Farmstay house for group Tour, Log Cabin for Family, and Log Cabin for Individuals. Group 1 (Traditional House) includes mainly the large accommodation establishments, i.e. those with ondoll style room providing meals and one shower room on family tourist, of original construction style house. Group 2 (Typical Farmhouse) encompasses accommodation establishments of Ondoll rooms and each bathroom providing meals. It includes, in other words, the tourist accommodations Known as "rural houses." Group 3 (Farmstay House for Group) has accommodation establishments of Ondoll rooms not providing meals and self cooking facilities, large room size over five persons. Group 4 (Log Cabin for Family) includes mainly the popular accommodation establishments, i.e. those with Ondoll style room with on shower room on family tourist, of western styled log house. While the accommodations in this group are not defined as regards type of construction, the group does include all the original Korean style construction, Finally, group 5 (Log Cabin for Individuals)includes those accommodations that are bedroom western styled wooden house with each bathroom. First Multinomial Logit model is estimated including all the explicative variables considered and taking accommodation group 2 as base alternative. The results show that the variables and the estimated values of the parameters for the model giving the probability of each of the five different types of accommodation available in rural tourism village in Korea, according to the socio-economic and trip related characteristics of the individuals. An initial observation of the analysis reveals that none of variables income, the number of journey, distance, and residential style of house is explicative in the choice of rural accommodation. The age and accompany variables are significant for accommodation establishment of group 1. The education and rural residential experience variables are significant for accommodation establishment of groups 4 and 5. The expenditure and marital status variables are significant for accommodation establishment of group 4. The gender and occupation variable are significant for accommodation establishment of group 3. The loyalty variable is significant for accommodation establishment of groups 3 and 4. The study indicates that significant differences exist among the individuals who choose each type of accommodation at a destination. From this investigation is evident that several profiles of tourists can be attracted by a rural destination according to the types of existing accommodations at this destination. Besides, the tourist profiles may be used as the basis for investment policy and promotion for each type of accommodation, making use in each case of the variables that indicate a greater likelihood of influencing the tourist choice of accommodation.

• Science of Emotion and Sensibility
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• v.11 no.1
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• pp.91-104
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• 2008

This study searches for an abstract aesthetic dimension that has an important response to preference by finding products excluded in past studies on aesthetics. Furthermore, the adjective image languages are examined, grouped and examined in order to also take into account the dimensions that are regarded as important for the selected product. Based on this, the preference trends will be inferred and the level of aesthetic dimensions that are import in the product will be found and presented based on its preference to create an accurate objective point. Based on the values of the levels of the aesthetic preference types and aesthetic elements found by this and presenting the preferred types in the design process, the objective of this study is to create designs with small chance of failure. In addition, it attempts to present a new direction for research of aesthetic dimensions.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.185-187
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• 2013

유비쿼터스 컴퓨팅이 생활의 일부가 되어가면서 정보의 양도 급속도로 늘어나고 있으며, 이로 인해 많은 데이터 속에서 정보를 찾아내는 기술이 부각되고 있다. 고객 기반의 협력적 필터링을 이용한 고객 선호도 예측 방법에서는 아이템에 대한 사용자의 선호도를 기반으로 이웃 선정 방법을 사용하므로 아이템에 대한 내용을 반영하지 못할 뿐만 아니라 희박성 문제를 해결하지 못하고 있다. 그리고 비슷한 선호도를 가진 일부 아이템의 정보를 바탕으로 하기 때문에 아이템의 속성은 무시하는 경향이 있다. 본 논문에서는 유비쿼터스 상거래에서 RFM(Recency, Frequency, Monetary) 분석 기반의 SOM을 이용한 군집방법을 제안한다. 제안 방법은 고객의 구매 데이터 기반의 유사한 속성의 데이터끼리의 클러스터링을 통해 보다 빠른 시간 내에 고객 성향에 맞는 추천이 가능한 구매 패턴 추출이 가능하다.

• Journal of the Korean Society for information Management
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• v.24 no.2
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• pp.89-104
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• 2007

This research attempts to give a personalized recommendation framework in large-sized music contents environment. Despite of existing studios and commercial contents for recommendation systems, large online shopping malls are still looking for a recommendation system that can serve personalized recommendation and handle large data in real-time. This research utilizes data mining technologies and new pattern matching algorithm. A clustering technique is used to get dynamic user segmentations using user preference to contents categories. Then a sequential pattern mining technique is used to extract contents access patterns in the user segmentations. And the recommendation is given by our recommendation algorithm using user contents preference history and contents access patterns of the segment. In the framework, preprocessing and data transformation and transition are implemented on DBMS. The proposed system is implemented to show that the framework is feasible. In the experiment using real-world large data, personalized recommendation is given in almost real-time and shows acceptable correctness.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.712-719
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• 2023

This study investigated the diversity of butterfly communities inhabiting agroecosystems and examined the effect of latitude and longitude. The ecological characteristics of butterflies inhabiting rural ecosystems, such as habitat preference and food plant range, were also examined. This study was conducted from 2019 to 2022, selecting 10 locations nationwide and conducting line transect surveys every two weeks for four years, confirming a total of 112 species and 21,901 individuals. There was no difference in the number of species and individuals by region, but there was a clear difference in community composition. The most abundant species in rural ecosystems were Pieris rapae, Polygonia c-aureum, Zizeeria maha, and Colias erate, in that order. There was no significant difference in the number of species and individuals by latitude and longitude, indicating no peninsula effect. Habitat preference showed that butterflies preferring grasslands and forest edges were much more common than those preferring the forest interior, and the food breadth was mostly oligophagous, followed by monophagous and polyphagous. Butterflies inhabiting agroecosystems had ecological characteristics that preferred open spaces such as grasslands and forest edges or relatively diverse foods, due to the similarity of the environmental characteristics of the survey points. Through this study, we believe that continuous monitoring is necessary to determine whether climate change, which is currently underway and habitat change are affecting butterflies in agroecosystems.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.1
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• pp.211-221
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• 2010

The purpose of this study is to verify if there is any difference on researchers' language preference for citing documents in different subject fields. 8 scientific journals were selected one of each from 8 main categories, given by National Research Foundation of Korea. Five variables including the rate of citing domestic and foreign documents for each journals were choose, and the differences on language preference among researchers of different subject fields were checked by implementing ANOVA. As a result, there are some differences on language preference, in terms of the average number and percentage of foreign documents, among researchers, based on their subject background. It also found that 8 subject categories divided into 4 small clusters.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.527-535
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• 2020

The purpose of research is to examine mothers with elementary school children in Chungcheong and the convergence effect of recognition of food labeling standards on purchasing behavior. A two-step cluster analysis was performed for group classification according to the purchase behavior of processed foods and the collection was determined by Schwarz's BIC criteria. Three types were determined: "convenience pursuit," "large mart preference," and "high cost reverse purchase". The proportion of college graduates in 'large mart preference' was higher, the proportion of employment mothers in 'high cost reverse purchase' was higher, and the need for food labeling standards was higher in 'large mart preference'. 'Shelf life' was recognized as the most important item. 'Large market preference' scored higher in 'used materials' and 'food additives', 'nutrition labelling'. In order to improve the purchasing behavior of processed foods, above all else, it is necessary to develop customized educational media that can be easily applied to real life.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.385-392
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• 2002

Collaborative filtering method for personalization can suggest new items and information which a user hasn t expected. But there are some problems. Not only the steps for calculating similarity value between each user is complex but also it doesn t reflect user s interest dynamically when a user input a query. In this paper, classifying users by their interest makes calculating similarity simple. We propose the a1gorithm for readjusting user s interest dynamically using the profile and Bayesian learning. When a user input a keyword searching for a item, his new interest is readjusted. And the user s profile that consists of used key words and the presence frequency of key words is designed and used to reflect the recent interest of users. Our methods of adjusting user s interest using the profile and Bayesian learning can improve the real satisfaction of users through the experiment with data set, collected in University s library. It recommends a user items which he would be interested in.