• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.2
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• pp.163-172
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• 2012

In this study, It is assumed that customers are both usage unit of spare parts and stores of displaying and selling the goods that are installation unit of having the spare parts. The demand pattern through the effective order of spare parts and issue list in installation unit is investigated based on the assumption. Current ASL (Authorized Stockage List) selection of the army has been conducted in the way of using the analysis result of real usage experiences on spare parts used during the Korea War. For this study, ASL selection criteria and procedures based on army regulations and field manuals are specified. Since the traditional method does not presents the association analysis on spare parts used for the current equipment operating and does not have the clear criterion and analysis system about the ASL selection, in order to solve these problems, it was carried out that the association rule is employed for analyzing relationship between the effective order and issue list of the spare parts in point of the spare parts between usage unit and occurring month about purchase spare parts based on the star-schema table. Finally the new ASL selection way using the analysis result is proposed.

• Information Systems Review
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• v.24 no.4
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• pp.1-22
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• 2022

The transition to a non-face-to-face consumer society has rapidly occurred since Covid-19. The need for a subdivided urban logistics policy centered on courier delivery, a life-friendly last-mile logistics service, has been raised. This study proposes a SNS-based method that can analyze the demand relationship by region and product, respectively. We extend the market basket network (MBN) and co-purchased product network (CPN), find product category patterns, and confirm regional differences by using delivery order data. Our results imply that SNA analysis can be effectively applied to inventory distribution or product (SKU) selection strategies in urban logistics.

• Management & Information Systems Review
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• v.30 no.2
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• pp.257-275
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• 2011

The price relationship between the futures market and the underlying spot market has attracted the attention of academics, practitioners, and regulators due to their roles during periods of turbulence in financial markets. The purpose of this paper is to investigate the dynamic of price relationship(or lead-lag relationship) between Korean Treasury Bond futures market and spot market. To examine the nature of the price relationship, descriptive statistics, serial correlation, and cross-correlation are used as a preliminary statistics in the Korean Treasury Bond spot and futures market. Next, following Stoll-Whaley(1990) and Chan(1992), the multiple regression method is used to examine the lead-lag patterns between the two markets. The empirical results are summarized as follows. The mean returns of spot markets and future markets are positive(+) and negative(-) respectively and the standard deviation of both stock and futures returns increase through the sub-periods. For the most periods, there is negative skewness in the both markets. The zero excess kurtosis due to the heavy tails of the distribution are relatively large. The autocorrelations in the spot returns for the sample periods are positive in time lag 1, but the autocorrelations in the future returns shows no significant evidence. The results of the daily cross-correlations between the KTB spot and futures returns indicate that a lead-lag relationship don't exist for price changes of futures and spot markets as a preliminary analysis. Finally, empirical results of regression analysis for both market indicate that there is no evidence that the KTB futures lead the KTB spot market, or the KTB spot market lead the KTB futures market. These results are robust for all sub-periods.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.311-316
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• 2010

This paper presents a weight adjusted association rule mining algorithm (WARM). Assigning weights to each strategic factor and normalizing raw scores within each strategic factor are the key ideas of the presented algorithm. It is an extension of the earlier algorithm TSAA (transitive support association Apriori) and strategic importance is reflected by considering factors such as profit, marketing value, and customer satisfaction of each item. Performance analysis based on a real world database has been made and comparison of the mining outcomes obtained from three association rule mining algorithms (Apriori, TSAA, and WARM) is provided. The result indicates that each algorithm gives distinct and characteristic behavior in association rule mining.

• Knowledge Management Research
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• v.19 no.4
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• pp.59-76
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• 2018

With the arrival of the big data era, customer data and data mining analysis have gradually dominated the process of Customer Relationship Management (CRM). This phenomenon indicates that customer data along with the use of information techniques (IT) have become the basis for building a successful CRM strategy. However, some companies can not discover valuable information through a large amount of customer data, which leads to the failure of making appropriate business strategy. Without suitable strategies, the companies may lose the competitive advantage or probably go bankrupt. The purpose of this study is to propose CRM strategies by segmenting customers into VIPs and Non-VIPs and identifying purchase patterns using the the VIPs' transaction data and data mining techniques (K-means clustering and association rules) of online shopping mall in Korea. The results of this paper indicate that 227 customers were segmented into VIPs among 1866 customers. And according to 51,080 transactions data of VIPs, home product and women wear are frequently associated with food, which means that the purchase of home product or women wears mainly affect the purchase of food. Therefore, marketing managers of shopping mall should consider these shopping patterns when they build CRM strategy.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.253-258
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• 2010

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

• 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 Korea Society of Industrial Information Systems
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• v.18 no.2
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• pp.47-57
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• 2013

Today a lot of functional areas of a firm are operated on the Web. Online shopping malls analyze web log recording customers' activities on the web to connect them to business outcomes. Not only commercial websites, but online news sites also need to collect and analyze web logs to understand their news readers' interest. However, little research has been performed yet. In this research we mined the web access log of an online news website and conduct Market Basket Analysis to uncover the association rules among the categories of news articles. The research is composed of two stages: 1) Identifying the individual session of a visitor; 2) Mining association rule from news articles read by each session. We gather 7-day access logs two times. The results of log mining and meanings of association rules are suggested with managerial implications in conclusion section.

• Journal of the Korea Computer Industry Society
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• v.2 no.7
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• pp.945-952
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• 2001

In consequence of expansion of internet users, electronic commerce is becoming a new prototype for marketing and sales, arid most of electronic commerce sites or internet shopping malls provide a rich source of information and convenient user interfaces about the organizations customers to maintain their patrons. One of the convenient interfaces for users is service to recommend products. To do this, they must exploit methods to extract and analysis specific patterns from purchasing information, behavior and market basket about customers. The methods are association rules and sequential patterns, which are widely used to extract correlation among products, and in most of on-line electronic commerce sites are executed with users information and purchased history by category-oriented. But these can't represent the diverse correlation among products and also hardly reflect users' buying patterns precisely, since the results are simple set of relations for single purchased pattern. In this paper, we propose an efficient mining technique, which allows for multiple purchased patterns that are category-independent and have relationship among items in the linked structure of single pattern items.

• Journal of Applied Biological Chemistry
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• v.57 no.3
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• pp.235-242
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• 2014

The purpose of this study is the establishment of scientific processes for making food safety policies. Thus, we investigated pesticide residue level of the agricultural commodities from market, and performed risk assessment. Fifteen agricultural items are chosen based on the frequency of Korean consumption. The samples were collected from 9 cities where populations are more than one million. Total 283 active ingredients were monitoring ( total sample number =232). Single-analysis of target pesticides was for three kinds of possible growth regulators and the multicomponent analysis was for 280 kinds of pesticides, a total of 283 species were selected to perform the pesticide residues. Before monitoring the analytes, the improvements of the analytical methods were done by method validations under the CODEX analytical method development guidelines and can produce metrics that represent the international standards applied in accordance with the guidelines. In addition to residual pesticides detected during monitoring we compare the ADI to EDI values using detected result and dietary consumption data which is extracted from annual market basket survey. The 163 samples were non-detected in the total 232 samples so it means that every agricultural commodity will residual pesticides-free in 70.3%. The detected residual pesticides showed for a total of 69 cases (29.7%). Two of samples violate Korean MRL (0.9%). The ratio of EDI compared to ADI resulted in only from 0.00087 to 0.902%. In result, we can assume that all detected residual pesticides are very safe level and current policies of Korean pesticides control may be working.