• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.7
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• pp.1205-1219
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• 2010

This study analyzes the conditions and products of internet shopping malls that sell natural dyeing products. In this study, 98 natural dyeing internet shopping malls were selected. The results of this study are as follows. The locations of the internet shopping malls were Gyeonggi-do, Seoul, Jeollanam-do, Gyeongsangbuk-do, Gwang-ju (city), Daegu (city), Busan (city), Gyeongsangnam-do, and Jeju-do. The most frequent dyes of the natural dyeing products were loess, followed by charcoal, indigo, and persimmon. Indigo was most frequently used in Seoul, with loess and charcoal most frequent in Gyeonggi-do. Persimmon, indigo, loess, and charcoal were mainly used in Jeollanam-do, with persimmon and loess in the Gyeongsangbuk-do, and persimmon in Jeju-do. The highest ordered product categories were accessories, followed by adult clothing, interior decoration products, and bedding. The most frequent products were bedclothes, followed by scarves, female shirts, blouses, pillows, female jackets, and vests. Regarding the price of products, 150,000-200,000 won was the highest for the Saenghwal Hanbok, with 10,000-30,000 won for underwear, 30,000-60,000 won for accessories, and 100,000-150,000 won for bedding. Concerning product information, 58.2% of internet shopping malls offer the product size and almost half of them did not show the properties or directions for handling the product. Based on the research results, the problems of the conditions and products of natural dyeing internet shopping mall were derived. The results show that the natural dyes of internet shopping malls lacked regional symbolism, the products were not specialized, and product information was not fully offered to consumers. To solve these problems, the strategies for marketing the promotion of the natural dyeing internet shopping mall were, ‘Using natural dyes from local resources’, ‘Market oriented and specialized item design’, and ‘Offer right product information’.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• Korean Management Science Review
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• v.32 no.1
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• pp.65-82
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• 2015

Purchase dependence is a frequent phenomenon in retail shops and is characterized by the purchase of certain items together due to their unknown interior associations. Although this concept has been significantly examined in the marketing field (e.g. market basket analysis), it has largely remained unaddressed in operations management. Since purchase dependence is an important factor in designing inventory replenishment policies, this paper demonstrates the means of applying it to the partial backordering inventory model. Through computational analyses, this paper compares the performance of inventory models that either consider or ignore purchase dependence; the results demonstrate that inventory models that ignore purchase dependence incur more average cost per unit time than the model that considers purchase dependence, and the impact of purchase dependence can increase in significance as the item set becomes more closely correlated with regard to order demand.

• IE interfaces
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• v.5 no.2
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• pp.29-37
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• 1992

An approach to Just-In-Time for small job-shop-type manufacturers is presented. This is aimed at those who understand the pull production system, however, cannot afford either swift changeover in workcenters or frequent delivery to customers. First, as a production planning technique, a lot-sizing model entitled Multilevel Least Total Cost Model(MLTCM) is developed; The production order quantities of each components be those requiremensts of the end item production lot. The contribution of MLTCM is shown via simulation. Then, a framework of the Kanban operations is designed; A Production Control Kanban is introduced as a communication tool with Production Kanban in a job-shop.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.275-288
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• 2015

Purchase dependence is a frequent phenomenon in retail shops and is characterized by the purchase of certain items together due to their unknown interior associations. Although this concept has been significantly examined in the marketing field (e.g. market basket analysis), it has largely remained unaddressed in operations management. Since purchase dependence is an important factor in designing inventory replenishment policies, this paper demonstrates the means of applying it to the partial backordering inventory model. Through computational analyses, this paper compares the performance of inventory models that either consider or ignore purchase dependence; the results demonstrate that inventory models that ignore purchase dependence incur more average cost per unit time than the model that considers purchase dependence, and the impact of purchase dependence can increase in significance as the item set becomes more closely correlated with regard to order demand.

• Journal of Information Technology Services
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• v.6 no.2
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• pp.113-123
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• 2007

In ubiquitous computing environment such as ubiquitous marketplace (u-market), there is a need of providing context-based personalization service while considering the nomadic user preference and corresponding requirements. To do so, the recommendation systems should deal with the tremendous amount of context data. Hence, the purpose of this paper is to propose a novel recommendation method which provides the products-group list of the customers in u-market based on the shopping intention and preferences. We have developed FREPIRS(FREquent Purchased Item-sets Recommendation Service), which makes recommendation listof product-group, not individual product. Collaborative filtering and apriori algorithm are adopted in FREPIRS to build product-group.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.301-302
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• 2013

연관 규칙 탐사는 이산적인 항목들을 포함하는 트랜잭션 데이터에 존재하는 항목 간 동시 발생 관계를 찾아내는 데 그 목적을 두고 있다. 연관 규칙은 {전항}${\rightarrow}${후항}의 형태를 갖고, 전, 후항은 모두 사전에 정의된 지지도 하한을 만족하는 빈발 항목 집합으로 구성된다. 연관 규칙 탐사에서 문제가 되는 것은 일반적으로 탐사되는 빈발 항목 집합의 개수가 많아지면서 규칙의 개수도 많아지고, 이들 사이에 중복성이 존재한다는 점이다. 따라서 단순히 지지도나 신뢰도 순으로 빈발 항목 집합이나 규칙을 나열하기보다는 항목들의 연관성을 고려하는 것이 분석자에게 보다 도움이 될 수 있다. 본 논문에서는 이를 위하여 연관 규칙 탐사와 함께 계층 군집 분석을 실시하여 항목들 간 연관성을 정리하고, 이를 토대로 빈발 항목 집합들을 나열하는 방법을 제안하고자 한다.

• Journal of the Korea Furniture Society
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• v.17 no.3
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• pp.45-55
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• 2006

The change of consumers' lifestyles causes frequent study on beds along with growth of economy, improvement of education and increase of dwelling in new towns and nuclear family. Beds are frequently developed in recent years since consumers' lifestyles increasingly change caused by transformation of types of dwelling, influence of western lifestyle, and increase uses of a bed. Also, this transformation causes needs for research on bed designs. A Bed is an essential item in a life since people spend one third of a day in Also, in contemporary, the ages of consumers are varied from infants to seniors. The study examines the importance of beds which playa major role in households. Moreover, the purpose of this study of beds focused on surface materials, colors, and designs is suggesting bases for future developments.

• Journal of the Korean Home Economics Association
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• v.42 no.1
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• pp.153-166
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• 2004

The purpose of this study was to examine the internet shoppers' new purchase behavior, to examine the general purchase behavior(i.e., purchase pattern, preference), and to examine the related factors to promotion strategies(i.e., e-mail, event) of internet shopping mall. Focus group interviews were done with 40 internet shopping-mall users on May, 2003 for the data collection. Data were analyzed by content analysis and descriptive statistics(i.e., frequency, percent). The results of this study were as following. First, competitive price, accurate product and service information and convenience were considered as important factors in the new purchase behavior among internet shoppers. Second, the more frequent purchasing time through the internet shopping mall were on weekdays rather than weeekends and the most preferred information search engine were category type, item type, and price type in order. Third, e-mails from internet shopping mall were most likely opened by internet shoppers, that is to say, e-mail can be the efficient communication tool as well as the possible promotion strategies. Specifically, the title of email was considered as an important factor to approach the target consumers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1327-1331
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• 2010

Association rules are very useful and interesting patterns for discovering preferences of each person in digital-content services. The Apriori algorithm is an influential algorithm for mining frequent itemsets for association rules. However, since this algorithm does not take into account reference times of each content as an important support factor, it cannot be used to extract associations among time-based data. This paper proposes an augmented Apriori algorithm discovers association rules using both frequencies and usage times of each item.