• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.271-280
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• 2005

This study is to elicit the factors affected on the medical examination in the tra야tional medicine using the technical method of the decision tree and characterize the Patient subject by clustering analysis technique. And to draw results from the association analysis between the form of diseases in the re-hospitalized Patient group. The obtained results were analyzed for their effect on the hospital Profits. Thus. through application of the database marketing to the data mining technique in the tradition리 medicine, the characteristics of patient clients for the objective induction of factors affected on the hospital Fronts can be identified. Practical application of the database marketing as presented in this study will bring about a fundamental efficiency of hospital management and vitalization.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.2
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• pp.159-170
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• 2010

A survey was conducted with 155 professors in 23 universities in Seoul. The survey investigated recognition and the present condition of FISP among those professors, the results of which were analyzed in terms of three aspects: recognition of FISP, utility and implementation of FISP, and FISP users' satisfaction and opinions. of professors, FISP users were most frequently using it to write a treatise, while non-FISP users failed to use it because they had no information of its existence. Both FISP and non-FISP users significantly favored implementation of FISP and showed very positive intention of using it.

• The KIPS Transactions:PartA
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• v.10A no.5
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• pp.487-492
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• 2003

To satisfy the requirements for QoS of Users using multimedia content stream service, it is required to control mechanism for QoS based on class priority, URFA classifies the user by two classes (super class, base class) and controls the admission ratio of user's requests by user's class information. URFA increases the admission ratio class and utilization ratio of stream server resources.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.239-254
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• 2006

Location-based advertising or application has been one of the drivers of third-generation mobile operators' marketing efforts in the past few years. As a result, many studies on location-based marketing or advertising have been proposed for recent several years. However, these approaches have two common shortcomings. First. most of them just suggested the theoretical architectures, which were too abstract to apply it to the real-world cases. Second, many of these approaches only consider service provider (seller) rather than customers (buyers). Thus, the prior approaches fit to the automated sales or advertising rather than the implementation of CRM. To mitigate these limitations, this study presents a novel advertisement recommendation model for mobile users. We call our model MAR-CF (Mobile Advertisement Recommender using Collaborative Filtering). Our proposed model is based on traditional CF algorithm, but we adopt the multi-dimensional personalization model to conventional CF for enabling location-based advertising for mobile users. Thus, MAR-CF is designed to make recommendation results for mobile users by considering location, time, and needs type. To validate the usefulness of our recommendation model. we collect the real-world data for mobile advertisements, and perform an empirical validation. Experimental results show that MAR-CF generates more accurate prediction results than other comparative models.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.4
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• pp.293-298
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• 2004

To satisfy the requirements for QoS and acceptance ratio of the users using multimedia content service, it is required to control mechanism for QoS assurance and allocation of the stream server' resources based on CoS(Class of Service). To compare performance of the algorithm, we have classified the user by two classes (super class, base class) and control the acceptance ratio of user's requests by user's class information. We have experimented the test of network resources and test of processing time under server/client environment and agent environment. MA-URFA based on agent increases the acceptance ratio of super class and utilization ratio of network resources.

• Information Systems Review
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• v.8 no.2
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• pp.119-138
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• 2006

Association Rule Mining has been successfully used for mining knowledge in static environment but it provides limited features to discovery time-dependent knowledge from multi-point data set. The aim of this paper is to develop a methodology which detects changes of customer behavior automatically from customer profiles and sales data at different multi-point snapshots. This paper proposes a procedure named 'Recursive Change Mining' for detecting continuous change of customer purchase behavior. The Recursive Change Mining Procedure is basically extended association rule mining and it assures to discover continuous and repetitive changes from data sets which collected at multi-periods. A case study on L department store is also provided.

• CRM연구
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• v.1 no.1
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• pp.23-48
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• 2006

Internet B2C marketplace such as 'Auction', 'G Market', 'Daum onket' etc.(called open market) has grown sharply in sales about yearly 100% rate increase in recent 1-2 years in Korea. Although Open marketplace has much reputation and the customer loyalty, almost of merchants participated in open market, which size in sales are medium/small, has poor reputation and trust. Consumers, who have to get in touch with untrustworthy merchants for trade in openmarket, perceive high trade risk which undergo the monetary damages such as 'merchandise never received'. This examines what factors consumer trust develop in online marketplace. This study explores several perceived risk factors in an open market by factor analysis and multi-regression to prove the relationships between the degree of trust for the merchants and the perceived trade risk. 133 data collected from the open market user data in this survey. In this study, the consumer's trade satisfaction in open market is low by 4.2 point degree of 7 point likert scale. and perceives 4 trade risk factors such as (1) 'failure to honor warranty or guarantee' (2) 'defective/poor goods in quality' (3) 'merchandise never received or received late' (4) 'poor information'. the degree of merchant's trust has significant relationship with the degree of perceived risk(sig. = 0.0000, $R^2=.327$) We find that the open market has to enhance the relationship marketing of trust by developing the strategies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.167-175
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• 2024

Due to the Fourth Industrial Revolution, ICT(Information and Communication Technology) industry is becoming more important and sophisticated than ever. In B2B based ICT industry demand forecasting by analyzing the previous customer data is so important. RFM, one of customer relationship management models is a marketing technique that evaluates Recency, Frequency and Monetary value to predict customers behavior. RFM model has been studied focusing on the B2C based industry. On the other hand there is a lack of research on B2B based technology industry. Therefore this study applied it to B2B based high technology industry and considered T(technology collaboration) value, which are identified as important factors in the technology industry. To present an improved model for market performance in B2B technology industry, an empirical study was conducted on comparing the accuracy of the traditional RFM model and the improved RFM-T model. The objective of this study is to contribute to market performance by presenting an improved model in B2B based high technology industry.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.139-157
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• 2002

The rapid growth of e-commerce has made both companies and customers face a new situation. Whereas companies have become to be harder to survive due to more and more competitions, the opportunity for customers to choose among more and more products has increased. So, the recommender systems that recommend suitable products to the customer have an important position in E-commerce. This research introduces collaborative filtering based recommender system which helps customers find the products they would like to purchase by producing a list of top-N recommended products. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is used to select target customers, who have high possibility of purchasing recommended products. We applied the recommender system to a Korean department store. The methodology is evaluated with the analysis of a real department store case and is compared with other methodologies.

• Information Systems Review
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• v.21 no.4
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• pp.31-45
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• 2019

Despite the expansion of the market of performing arts and culture, small and medium size theaters are still experiencing difficulties due to poor accessibility of information by consumers. This study proposes a machine learning based genre recommendation system as an alternative to enhance the marketing capability of small and medium sized theaters. We developed five recommendation systems that recommend three genres per customer using customer master DB and transaction history DB of domestic venues. We propose an optimal recommendation system by comparing performances of recommendation system. As a result, the recommendation system based on the ensemble model showed better performance than the single predictive model. This study applied the personalized recommendation technique which was scarce in the field of performing arts and culture, and suggests that it is worthy enough to use it in the field of performing arts and culture.