• Proceedings of the Safety Management and Science Conference
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• 2002.05a
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• pp.145-150
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• 2002

인터넷을 기반으로 하는 사이버교육은 활발 전개되고 있다 하지만 사이버교육에서의 CRM 적용사례는 부족한 현실이다. 본 연구는 RFM, Prediction, 고착도, 연관규칙, 분류규칙등 데이터 마이닝기법들을 활용하여 학습자의 수준에 맞는 강의추천전략을 제안했다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.417-422
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• 2003

The KOMPSAT-2 MSC(Multi-Spectral Camera), with high spatial resolution, is currently under development and will be launched in the end of 2004. A sensor model relates a 3-D ground position to the corresponding 2-D image position and describes the imaging geometry that is necessary to reconstruct the physical imaging process. The Rational Function Model (RFM) has been considered as a generic sensor model. form. The RFM is technically applicable to all types of sensors such as frame, pushbroom, whiskbroom and SAR etc. With the increasing availability of the new generation imaging sensors, accurate and fast rectification of digital imagery using a generic sensor model becomes of great interest to the user community. This paper describes the procedure to generation of the RPC (Rational Polynomial Coefficients) for KOMPSAT-2 MSC.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.269-274
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• 2004

Recent adoption of the generalized sensor model to IKONOS and Quickbird satellite imagery have promoted various research activities concerning alternative sensor models which can replace conventional physical sensor models. For example, there are the Rational Function Model(RFM), the Direct Linear Transform(DLT) and the polynomial transform. In this paper, the DLT model which uses just a few number of GCPs was suggested. To evaluate the accuracy of the proposed DLT model, the RFM using 35 GCPs and the bias compensation method(Fraser et al., 2003) were compared with it. Quantitative evaluation of 3B positioning results were performed with independent check points and the digital elevation models(DEMs). In result, a 1.9- to 2.2-m positioning accuracy was achieved for modeling and DEM accuracy is similar to the accuracy of the other model methods.

• Spatial Information Research
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• v.19 no.2
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• pp.65-70
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• 2011

Advertizing postal system combined with GIS and temporal/spatial mining techniques has been developed to activate advertizing service and conduct marketing campaign efficiently. In order to select customers accurately, this system provide purchase propensity information using sequential, cyclicpatterns and lifesytle information through RFM analysis and clustering technique. It is possible for corporate mailer to do customer oriented marketing campaign with the advertizing postal system as well as 'one-stop' service including target customer selection, mail production, and delivery request.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.109-120
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• 2006

Huge information has been made due to the current computing environment and could not be acceptable. People want the information which they can understand and accept easily. They may want not only simple information but also knowledge. That is why data mining becomes a center of information. We use RFM analysis in order to create customer score. Customers are classified into five groups(most oxcellenrexcellenycommoflowerilowest) for a various marketing activities. We can found the significant patterns in each group, and classify customers from loyal customers to leaving customers in the near future by the indirect data mining(e.g. association analysis) and the direct data mining(e.g. decision tree, logistic regression analysis, etc.), which are named in this study. Our research focuses on the advanced models by applying the association rules in data mining. Our results indicate that the indirect data mining and the direct data mining seem to have same outputs, but the former shows more clear pattern then the latter one.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Information Technology Applications and Management
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• v.20 no.3_spc
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• pp.219-230
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• 2013

Real time accessiblity and agility in Ubiquitous-commerce is required under ubiquitous computing environment. The Research has been actively processed in e-commerce so as to improve the accuracy of recommendation. Existing Collaborative filtering (CF) can not reflect contents of the items and has the problem of the process of selection in the neighborhood user group and the problems of sparsity and scalability as well. Although a system has been practically used to improve these defects, it still does not reflect attributes of the item. In this paper, to solve this problem, We can use a implicit method which is used by customer's data and purchase history data. We propose a new clustering method of weighted preference for customer using k-means clustering and Bayesian network in order to improve the accuracy of recommendation. To verify improved performance of the proposed system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.535-540
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• 2006

This paper presented an effective strategy to extract the buildings and to reconstruct 3-D buildings using high-resolution multispectral stereo satellite images. Proposed scheme contained three major steps: building enhancement and segmentation using both BDT (Background Discriminant Transformation) and ISODATA algorithm, conjugate building identification using the object matching with Hausdorff distance and color indexing, and 3-D building reconstruction using photogrammetric techniques. IKONOS multispectral stereo images were used to evaluate the scheme. As a result, the BDT technique was verified as an effective tool for enhancing building areas since BDT suppressed the dominance of background to enhance the building as a non-background. In building recognition, color information itself was not enough to identify the conjugate building pairs since most buildings are composed of similar materials such as concrete. When both Hausdorff distance for edge information and color indexing for color information were combined, most segmented buildings in the stereo images were correctly identified. Finally, 3-D building models were successfully generated using the space intersection by the forward RFM (Rational Function Model).

• Childhood Kidney Diseases
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• v.15 no.1
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• pp.38-48
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• 2011

Purpose: Focal segmental glomerulosclerosis (FSGS) is the most common glomerulopathy causing pediatric renal failure. Since specific treatment targeting the etiology and pathophysiology of primary FSGS is yet elusive, the authors explored the pathophysiology of FSGS by transcriptome analysis of the disease using an animal model. Methods: FGS/kist strain, a mouse model of primary FSGS, and RFM/kist strain, as control and the parent strain of FGS/kist, were used. Kidney tissues were harvested and isolated renal cortex was used to extract mRNA, which was run on AB 1700 mouse microarray chip after reverse transcription to get the transcriptome profile. Results: Sixty two genes were differentially expressed in FGS/kist kidney tissue compared to the control. Those genes were related to cell cycle/cell death, immune reaction, and lipid metabolism/vasculopathy, and the key molecules of their networks were TNF, IL-6/4, IFN${\gamma}$, TP53, and PPAR${\gamma}$. Conclusion: This study confirmed that renal cell death, immune system activation with subsequent fibrosis, and lipid metabolism-related early vasculopathy were involved in the pathophysiology of FSGS. In addition, the relevance of methodology used in this study, namely transcriptome profiling, and Korean animal model of FGS/kist was validated. Further study would reveal novel pathophysiology of FSGS for new therapeutic targets.