• Journal of the Korea Society of Computer and Information
• /
• v.17 no.2
• /
• pp.197-206
• /
• 2012

A exisiting recommedation system using association rules has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy as well. In this paper, using a Implicit method which is not used user's profile for rating, we propose the personalized recommendation system which is a new method using the FP-tree mining based on RFM. It is necessary for us to keep the analysis of RFM method and FP-tree mining to be able to reflect attributes of customers and items based on the whole customers' data and purchased data in order to find the items with high purchasability. The proposed makes frequent items and creates association rule by using the FP-tree mining based on RFM without occurrence of candidate set. We can recommend the items with efficiency, are used to generate the recommendable item according to the basic threshold for association rules with support, confidence and lift. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.1 s.39
• /
• pp.293-300
• /
• 2006

The Recommender systems help users to find and evaluate items of interest. Such systems have become powerful tools in the domains from electronic commerce to digital libraries and knowledge management. Sellers can recommend products to customers with the prediction of future buying behavior on the basis of the consumer's population statistics and past selling behavior. In this paper, we are describing the design and the development of personalization recommender system which increases satisfaction level of customers by searching products to reflect the pattern and propensity of customers properly. The suggested system supplies the real-time analysis service to predict the customers purchase situation by applying the association rule of the data mining.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.34 no.3
• /
• pp.537-552
• /
• 2024

Domestic nuclear power plants operate under the establishment of the "Information System Security Regulations" in accordance with the Nuclear Safety Act, introducing and implementing a cybersecurity system that encompasses organizational structure as well as technical, operational, and managerial security measures for assets. Despite attempts such as phased approaches and alternative measures for physical protection systems, the reduction in managed items has not been achieved, leading to an increased burden on security capabilities due to limited manpower at the site. In the main text, an analysis is conducted on Type A1 assets performing nuclear safety functions using Maintenance Rules (MR) and EPRI Technical Assessment Methodology (TAM) from both a maintenance perspective and considering device characteristics. Through this analysis, approaches to re-evaluate the impact of cyber intrusions on asset functionality are proposed.

• Journal of the Korean Society of Costume
• /
• v.58 no.3
• /
• pp.149-162
• /
• 2008

The Regular rule of Sang-uiwon served as a manual of Royal Attires. According to the procedures, like as making letters about the affairs, consulting, and permission, Royal ceremonial attire was made and presented to the royal family. The materials for the Royal informal dress were presented in accordance with usual tributes. There was no difference in informal dress between the royal family and noble class. But the name of items was different such as Goa du[man's jacket], Go ui[woman's jacket], etc. The royal family continued to wear old days dress as akjurm and noui, which were not worn by common people any more, as a means of differentiating clothes. Bub-bok, which was designed only for key figures of the royal family such as the king, crown prince, queen, and crown princess, was the best status symbol. Because of its highly limited example of wearing, bub-bok was the authority of the wearer itself; with only difference in color, pattern, and material depending on social status. Yong-po is the most frequently worn by the Royal men. Yong-po worn with jong-lip served as yung-bok or gun-bok, and iksun-gwan functioned as sang-bok. Royal Attire for men was clearly divided into Yong-po as sang-bok, bub-bok as myun-bok and gangsa-po, while jeok-ui for women functioned as both sang-bok and bub-bok. However, the use of jeok-ui was defined by differentiate sang-bok from bub-bok like as the pattern of Hyung-bae, number of embroidered round badges, shoes and ornaments.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.4
• /
• pp.857-868
• /
• 2014

Data mining is the representative analysis methodology in the era of big data, and is the process to analyze a massive volume database and summarize it into meaningful information. Association rule technique finds the relationship among several items in huge database using the interestingness measures such as support, confidence, lift, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. Moreover, we can not know association direction by them. This paper propose causally confirmed association thresholds to compensate for these problems, and then check the three conditions of interestingness measures. The comparative studies with basic association thresholds, causal association thresholds, and causally confirmed association thresholds are shown by simulation studies. The results show that causally confirmed association thresholds are better than basic and causal association thresholds.

• Science of Emotion and Sensibility
• /
• v.14 no.1
• /
• pp.17-26
• /
• 2011

Health care service's added value and sustainability has been formed, through the product developing about sports equipment and special equipment for disabled in order to improve the life quality, with the increasing population of elderly and the attention about health care. This research's design and 3 testing sections has been done according to design process for design development of functional bike. 1st test is done through researching from 4 aspects: structure, aesthetic, function and using. In the 2nd testing, 10 universal design items were used to evaluate 10 modeling samples, and sample F which has high evaluation overall was chosen. In 3rd test, evaluation was done from the user service scene about the mock-up with 1/4 scale size. PPP (product performance program) which is constructed with 60 evaluation items about functional bike's service was tested, and these items were fixed through discussing with experts. Through the result we knew the aesthetic elements had relationship with proportion, unity and typicality. In 10 items (55 survey items), the scores of items with physical exposure's minimization, simple and intuitively usage showed high, on the contrary, the other items' scores was very low, such as information delivery's consideration and thought, failure preventing. The evaluation will be done once more by health care experts, designers and elderly together if the physical model could be made for getting accurate measurement about above test result in the future.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.7
• /
• pp.768-774
• /
• 2016

This paper deals with a method to recommend the combination of items as a group according to similarity to handle application area such as fashion and cooking, while the previous methods recommend single item such as a book, music or movie. Collaborative filtering is a method to recommend an item selected by users with similar tendency based on similarity between users. In this paper, the proposed method generates a set of frequent items based on collaborative filtering and association rules and recommends a group by similarity between groups. To show the validity of the proposed method, experiments are performed with purchase data collected from e-commerce for four months.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.11
• /
• pp.14-18
• /
• 1997

In this paper, we suggested and made a classifier or qualitative and quantitative analysis in urine analysis system. Input variable number and fuzzy membership function was made from determination of standard sample, and the fuzzy rules were determined by the analysis of spectroscopic properties of pads in strip. Fuzzy classifier used in urine analysis system was evaluated or the standard samples in each items and degrees. Negative and positive response of urine test was classified in good property, but detail classification or quantitative analysis had 8% maximum error in each items. If fuzzy membership unction and generation of rule are supplemented, suggested fuzzy classifier can be applied to the clinical test.

• Journal of the Korean Society of Safety
• /
• v.36 no.2
• /
• pp.49-57
• /
• 2021

In the case of military supplies, any potential failure and causes of failures must be considered. This study is aimed at examining the failure modes of a rotorcraft landing system to identify the priority items. Failure mode and effects analysis (FMEA) is applied to the rotorcraft landing system. In general, the FMEA is used to evaluate the reliability in engineering fields. Three elements, specifically, the severity, occurrence, and detectability are used to evaluate the failure modes. The risk priority number (RPN) can be obtained by multiplying the scores or the risk levels pertaining to severity, occurrence, and detectability. In this study, different weights of the three elements are considered for the RPN assessment to implement the FMEA. Furthermore, the FMEA is implemented using a fuzzy rule base, similarity aggregation model (SAM), and grey theory model (GTM) to perform a comparative analysis. The same input data are used for all models to enable a fair comparison. The FMEA is applied to military supplies by considering methodological issues. In general, the fuzzy theory is based on a hypothesis regarding the likelihood of the conversion of the crisp value to the fuzzy input. Fuzzy FMEA is the basic method to obtain the fuzzy RPN. The three elements of the FMEA are used as five linguistic terms. The membership functions as triangular fuzzy sets are the simplest models defined by the three elements. In addition, a fuzzy set is described using a membership function mapping the elements to the intervals 0 and 1. The fuzzy rule base is designed to identify the failure modes according to the expert knowledge. The IF-THEN criterion of the fuzzy rule base is formulated to convert a fuzzy input into a fuzzy output. The total number of rules is 125 in the fuzzy rule base. The SAM expresses the judgment corresponding to the individual experiences of the experts performing FMEA as weights. Implementing the SAM is of significance when operating fuzzy sets regarding the expert opinion and can confirm the concurrence of expert opinion. The GTM can perform defuzzification to obtain a crisp value from a fuzzy membership function and determine the priorities by considering the degree of relation and the form of a matrix and weights for the severity, occurrence, and detectability. The proposed models prioritize the failure modes of the rotorcraft landing system. The conventional FMEA and fuzzy rule base can set the same priorities. SAM and GTM can set different priorities with objectivity through weight setting.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.27-42
• /
• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.