• 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 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.

• Korean Journal of Ecology and Environment
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• v.57 no.2
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• pp.83-91
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• 2024

This study utilizes association rule learning and clustering analysis to explore the co-occurrence and relationships within ecosystems, focusing on the endangered brackish-water snail Clithon retropictum, classified as Class II endangered wildlife in Korea. The goal is to analyze co-occurrence patterns between brackish-water snails and other species to better understand their roles within the ecosystem. By examining co-occurrence patterns and relationships among species in large datasets, association rule learning aids in identifying significant relationships. Meanwhile, K-means and hierarchical clustering analyses are employed to assess ecological similarities and differences among species, facilitating their classification based on ecological characteristics. The findings reveal a significant level of relationship and co-occurrence between brackish-water snails and other species. This research underscores the importance of understanding these relationships for the conservation of endangered species like C. retropictum and for developing effective ecosystem management strategies. By emphasizing the role of a data-driven approach, this study contributes to advancing our knowledge on biodiversity conservation and ecosystem health, proposing new directions for future research in ecosystem management and conservation strategies.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• 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.

• Korean Journal of Poultry Science
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• v.15 no.2
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• pp.81-91
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• 1988

The purpose of this research is to analyze the tendencies of poultry consumption in Korea. The information used is the data collected three times during the period from Sept. 1, 1985 to April 30, 1987 throughout the major cities. Those who participated in the survey are 2, 598 including housewives, nutritionists, cookers, group feeding institutions, woman's organizations, and the students of doing nutrition－related studies. Consumer preferences for poultry meat and eggs tend to move toward high quality and more strict sanitary standards. Following this line of consumer preference changes, the poultry product marketing supported by cold chain system is rapidly developing. Household consumption of poultry meat largely consists of hi－and semi－broilers but the household demand for these two broilers tends to decrease steadily over time. In general poultry meat consumption appears to be affected by consumer taste rather than market factors such as prices. In addition consumer choices are quite different depending on poultry meat parts which are preferred in order of drum sticks, wings, and breasts. In particular drum sticks are most preferred than any other parts. An important problem in poultry production is related to consumption seasonality since large part of poultry meat consumption is concentrated during the summer season. Another problem is associated with little development of cooking methods. At present there are two types of primary commercial cooking techniques, fries and samgaetang (boiled chicken with jinsang and rice). For promoting domestic poultry meat disposal and reducing the demand seasonality, new cooking methods should be developed and followed by more aggresive advertisements. In domestic egg trade, smaller packing units(i.e., 10 eggs per unit) tend to bi preferred to large ones (i.e., 30 eggs per unit). In consumers egg purchasing decisions nearness to the shops and convenience appear to be important factors. For egg shell colors consumers recognize that there is no difference in nutritional values. However, survey results show that consumers highest preference lies in eggs with brown color. Eggs are most popular among children and preferred in order of middle－and high－school students, 17-25 age people, and adults. Egg prices are concieved relatively cheap to its nutritional values. In house－holds eggs are consumed in the forms of fries, side dishes, and lunch basket dishes. However, high level of cholesterol content in eggs appears to be an important problem in promoting eggs consumption.