• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.49-56
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• 2017

Among the techniques for analyzing big data, the association rule mining is a technique for searching for relationship between some items using various relevance evaluation criteria. This associative rule scheme is based on the direction of rule creation, and there are positive, negative, and inverse association rules. The purpose of this paper is to investigate the applicability of various types of relatively causal strength measures to the types of association rules from the point of view of interestingness measure. We also clarify the relationship between various types of confidence measures. As a result, if the rate of occurrence of the posterior item is more than 0.5, the first measure ($RCS_{IJ1}$) proposed by Good (1961) is more preferable to the first measure ($RCS_{LR1}$) proposed by Lewis (1986) because the variation of the value is larger than that of $RCS_{LR1}$, and if the ratio is less than 0.5, $RCS_{LR1}$ is more preferable to $RCS_{IJ1}$.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1189-1197
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• 2013

Data mining is the process of analyzing a huge database from different perspectives and summarizing it into useful information. One of the well-studied problems in data mining is association rule generation. Association rule mining finds the relationship among several items in massive volume database using the interestingness measures such as support, confidence, lift, etc. Typical applications for this technique include retail market basket analysis, item recommendation systems, cross-selling, customer relationship management, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. This paper propose causal association thresholds to compensate for this problem, and then check the three conditions of interestingness measures. The comparative studies with basic and causal association thresholds are shown by numerical example. The results show that causal association thresholds are better than basic association thresholds.

• Journal of KIBIM
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• v.12 no.1
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• pp.42-54
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• 2022

BIM facilitates data transparency and consistency through three-dimensional parametric modeling and promotes the accurate managing and sharing of project information. In Korea, however, BIM-based detailed design of architectural interior finishes required during the Construction Documents phase increases the burden on architectural firms due to frequent design changes and manual workload. Therefore, the purpose of this study was to establish rule-based modules using parametric modeling that automates repetitive tasks that occur during the detailed design of interior finishing. Interviews with practitioners were conducted to analyze the major finishing elements. Of these floors, walls, and ceilings, which were the most rudimentary and common items, were selected as the subjects of the study. The modules developed in this study have two functions. One is to create new finish types, and the other is the automatic modeling of new types into rooms. For these functions, parameters that belonged to each finish and room element in a BIM model were analyzed and valid parameters directly used for parametric modeling were derived. Then, based on these parameters, rule-based modules for three elements, I.e., floors, walls, and ceilings were constructed with Revit Dynamo, and the effectiveness of the modules was verified with a pilot test. In conclusion, this study suggested a series of processes for automatic finishing to improve the efficiency of BIM-based architectural detailed design of finishes and to contribute in solving the chronic problems occuring during current design processes.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.335-346
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• 2015

The electronic commerce site (EC site) has become an important marketing channel where consumers can purchase many kinds of products; their access logs, including purchase records and browsing histories, are saved in the EC sites' databases. These log data can be utilized for the purpose of web marketing. The customers who purchase many product items are good customers, whereas the other customers, who do not purchase many items, must not be good customers even if they browse many items. If the attributes of good customers and those of other customers are clarified, such information is valuable as input for making a new marketing strategy. Regarding the product items, the characteristics of good items that are bought by many users are valuable information. It is necessary to construct a method to efficiently analyze such characteristics. This paper proposes a new latent class model to analyze both purchasing and browsing histories to make latent item and user clusters. By applying the proposal, an example of data analysis on an EC site is demonstrated. Through the clusters obtained by the proposed latent class model and the classification rule by the decision tree model, new findings are extracted from the data of purchasing and browsing histories.

• Korean Journal of Community Nutrition
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• v.8 no.1
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• pp.62-70
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• 2003

This article aims to summarize the development and application of menu engineering technique, 'Menu Engineering Modified by Preference (MEMP)'. The site selected for this project was a foodservice operation in Yonsei University residence hall. Sales and food costs data were collected from the daily sales reports for 1 month, and the survey of food preference was conducted during May, 1999. Statistical data analysis was completed using the SAS/Win 6.12 for descriptive analysis. The calculation for menu analysis were carried out with MS 2000 Excel spreadsheet program. This MEMP technique developed had 6 category criteria and 2 dimensions of the contribution margin (CM) and the menu mix modified% (MMM%) . The MMM% was calculated by the sales volumes and also weighted by food preference. The CM and MMM% for each item were compared with a mean menu CM as well as a 70% rule. Four possible classifications by MEMP were fumed out as 'STAR', 'PLOWHORSE', 'PUZZLE', 'DOG'. 'STAR' items were the most popular and profitable items and required to maintain rigid specifications for quality. The decision actions for 'PLOWHORSE' menu items which were relatively popular, but yield a low menu average CM included combining a plowhorse item with lower cost products and reducing the frequency of serving or serving size. There was a need for 'PUZZLE' items to be changed in the menu combination, improve recipe, and promote menu. The last DOG' items were desired to be deleted. This study demonstrates that menu information can be interpreted more easily with MEMP. The use of MEMP is therefore an effective way to improve management decisions about menu of university residence hall foodservice.

• Journal of Korean Library and Information Science Society
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• v.51 no.4
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• pp.211-230
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• 2020

The purpose of this study is to contribute to improving the efficiency of managing the database of the reports arising from the results of National R&D projects. To that end, the reports submitted by 49 agencies under the Ministry of Science and ICT were collected, and samples were selected for each institution. The samples and form of the final report and summary of the Enforcement Rule of the Framework Act on Science and Technology were compared, and the components and items to be entered were established. The final report's unique items were derived from the analysis of the state of connection with the National R&D information standard. The items to be entered are classified into major and optional according to their importance, and the location of the entry to be entered is suggested. If standardization of the elements and items is advanced as planned, it is expected to automate metadata extraction and improve the quality of report metadata when building a database.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.

• Journal of radiological science and technology
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• v.41 no.5
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• pp.493-504
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• 2018

The diagnostic radiation equipment is managed in accordance with the "Rules for Safety Management of Diagnostic Radiation Equipment" enacted in 1995. The equipments should be inspected before use and every three years after use in accordance with the [Appendix 1] of the same rule. The inspection standard has been maintained without particular revision since enacted. But, over the past two decades new types of equipments have been manufactured and used. So, it is necessary to revise [Appendix 1] by making inspection items and inspection standards. In this study, we revised the classification system of equipments and reviewed international standards of IEC 60601 series, IEC 61223 series and AAPM TG 18 On-line Report No.03. And identified the problem of current inspection standards. Through this, we revised, deleted and added the inspection items and inspection standard of each equipment to meet the domestic circumstances. As a result of the study, we reorganized the classification system of equipment which are current classified as 5 classes into 22 classes as X-ray system etc. (7 classes), CT system etc. (5 classes) and Dental X-ray system etc. (10 classes). And then, we developed 70 inspection items for 6 types of equipments according to the reorganized classification system of equipments. The inspection items and inspection standards derived from this study have been proposed to the KCDC and will be applied to the revision of the Rule's [Appendix 1]. Therefore, we expect to be used as reference materials for domestic medical center, inspection institutions, and equipment manufacturing import companies.

• Korean Journal of Child Studies
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• v.24 no.3
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• pp.73-85
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• 2003

The purpose of this study was to examine whether adolescents' parental rule obedience moderates the relationship between maternalmonitoring and problem behavior (drinking and vandalism). Participants were 398 adolescents attending a middle school (6th through 8th grades) in a semi-rural mid-Atlantic community in the USA. Data were collected by questionnaires including items regarding relationships with parents, problem behavior, and peer relationships. Hierarchical multiple regression was usedto analyze the data. Results indicate that maternal monitoring is negatively related with adolescent drinking and vandalism. However, the negative relationship varies as a function of adolescents' parental rule obedience. The relationship was stronger among adolescents with lower level of parental rule obedience than among adolescents with higher level of parental rule obedience. Findings from this study suggest that research on maternal monitoring during adolescence needs to take adolescents' personal characteristics into account to better understand the process. They also suggest that parents could lower the possibility that their children's will be involved in drinking and vandalism by maintaining quality relationships with their children because children are more likely to internalize their parents' rules and standards when they have positive relationships with parents.

• The Journal of Korean Association of Computer Education
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• v.11 no.1
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• pp.65-74
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• 2008

In this paper, we introduce an analysis system for answer dataset by using a data mining method. We analyze students' answer data collected from a test including multiple choice question items, and find associations between the items. Analysis of evaluation results based on our system will not only provide correct information on students' achievement levels but also provides a basis for modifying weaknesses of the evaluation procedures, question items, or teaching/learning procedures. Furthermore, it will enable us to improve the quality of question items for future use so that we can secure itemsets of high quality.