• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.3-5
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• 2006

Recent advances in the internet infrastructure have resulted in a large number of huge Web sites and portals worldwide. These Web sites are being visited by various types of users in many different ways. Among all the web page access sequences from different users, some of them occur so frequently that may need an attention from those who are interested. We call them frequent access patterns and access sequences that can be frequent the candidate patterns. Since these candidate patterns play an important role in the incremental Web mining, it is important to efficiently generate, add, delete, and search for them. This thesis presents a novel tree structure that can efficiently store the candidate patterns and a related set of algorithms for generating the tree structure adding new patterns, deleting unnecessary patterns, and searching for the needed ones. The proposed tree structure has a kind of the 3 dimensional link structure and its nodes are layered.

• Animal cells and systems
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• v.4 no.1
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• pp.91-94
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• 2000

Fingerprints of 114 Australian Aboriginal males and 90 females have been analyzed. Whorls are more frequent in males (56.7%) than in females (51.2%) and loops are less frequent in males (42.6%) than in females (47.0%). The index of pattern intensity displays a higher value in males (15.60) than in females (14.94). The bimanual differences both in males and females ave not statistically significant for the occurrence of patterns on the digits of the right and left hands. Also the difference between both sexes for the occurrence of patterns is not statistically significant. Incidences of actual symmetry on homologous digits represented 74.0% in males and 77.3% in females. The mean total ridge counts showed 156.65 $\pm$43.32 (M$\pmSD) in males and 148.6943.64 (M$\pmSD) in females, respectively. Conclusively, this study represents that the Australian Aborigines conform closely to the Polynesians in finger dermatoglyphics.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.89-95
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• 2015

Traditional frequent pattern mining discovers valid patterns with no smaller frequency than a user-defined minimum threshold from databases. In this framework, an enormous number of patterns may be extracted by a too low threshold, which makes result analysis difficult, and a too high one may generate no valid pattern. Setting an appropriate threshold is not an easy task since it requires the prior knowledge for its domain. Therefore, a pattern mining approach that is not based on the domain knowledge became needed due to inability of the framework to predict and control mining results precisely according to the given threshold. Top-k frequent pattern mining was proposed to solve the problem, and it mines top-k important patterns without any threshold setting. Through this method, users can find patterns from ones with the highest frequency to ones with the k-th highest frequency regardless of databases. In this paper, we provide knowledge both on frequent and top-k pattern mining. Although top-k frequent pattern mining extracts top-k significant patterns without the setting, it cannot consider both item quantities in transactions and relative importance of items in databases, and this is why the method cannot meet requirements of many real-world applications. That is, patterns with low frequency can be meaningful, and vice versa, in the applications. High utility pattern mining was proposed to reflect the characteristics of non-binary databases and requires a minimum threshold. Recently, top-k high utility pattern mining has been developed, through which users can mine the desired number of high utility patterns without the prior knowledge. In this paper, we analyze two algorithms related to top-k high utility pattern mining in detail. We also conduct various experiments for the algorithms on real datasets and study improvement point and development direction of top-k high utility pattern mining through performance analysis with respect to the experimental results.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.17-24
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• 2011

Collaborative filtering system is the clustering about user is built and then based on that clustering results will recommend the preferred item to the user. However, building user clustering is time consuming and also once the users evaluate and give feedback about the film then rebuilding the system is not simple. In this paper, genre pattern of movie recommendation systems is being used and in order to simplify and reduce time of rebuilding user clustering. A Frequent pattern networks is used and then extracts user preference genre patterns and through that extracted patterns user clustering will be built. Through built the clustering for all neighboring users to collaborative filtering is applied and then recommends movies to the user. When receiving user information feedback, traditional collaborative filtering is to rebuild the clustering for all neighbouring users to research and do the clustering. However by using frequent pattern Networks, through user clustering based on genre pattern, collaborative filtering is applied and when rebuilding user clustering inquiry limited by search time can be reduced. After receiving user information feedback through proposed user clustering based on genre pattern, the time that need to spent on re-establishing user clustering can be reduced and also enable the possibility of traditional collaborative filtering systems and recommendation of a similar performance.

• Journal of Korean Medicine for Obesity Research
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• v.8 no.2
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• pp.37-47
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• 2008

Objectives The aim of the study was to investigate the difference between pattern identification of premenopausal(n=39) and climacteric(n=40) korean obese and overweight women using Syndrome Differentiation Questionaire. Methods 39 premenopausal obese women(BMI${\geq}25kg/m^2$) and 40 climacteric overweight and obese women(BMI${\geq}23kg/m^2$) were recruited from October 2007 to March 2008 in Seoul, Korea. Subjects who had other disease were rejected. Basic anthropometry and body composition were measured. Every subjects were given and filled out the Syndrome Differentiation Questionaire, and we analyzed that using Fisher's exact test. Results 1. Premenopausal women showed high frequency of food accumulation pattern(43.6%), but in climacteric women, liver qi depression pattern was frequent(57.5%, p=0.021). 2. In weighted Syndrome Differentiation Questionaire score, Premenopausal women showed high frequency of food accumulation pattern(43.6%), but in climacteric women, liver qi depression pattern was frequent(47.5%, p=0.004). 3. There were no correlation between anthropometry and scores of the each patterns. Conclusions In this study, we can find out that the dietary factors play major roles in obesity of premenopausal women and emotional factors in obese climacteric women in the view of oriental pattern identification diagnosis. But it seemed that there lacked of consideration that reflected the degree of obesity in this Syndrome Differentiation Questionaire.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.237-248
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• 2009

Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.137-144
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• 2013

A sequential pattern mining is finding out frequent patterns from the data set in time order. In this field, a dynamic weighted sequential pattern mining is applied to a computing environment that changes depending on the time and it can be utilized in a variety of environments applying changes of dynamic weight. In this paper, we propose a new sequence data mining method to explore the stream data by applying the dynamic weight. This method reduces the candidate patterns that must be navigated by using the dynamic weight according to the relative time sequence, and it can find out frequent sequence patterns quickly as the data input and output using a hash structure. Using this method reduces the memory usage and processing time more than applying the existing methods. We show the importance of dynamic weighted mining through the comparison of different weighting sequential pattern mining techniques.

• Korean Journal of Community Nutrition
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• v.27 no.1
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• pp.1-11
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• 2022

Objectives: Away-from-home (AFH) eating has been associated with poor diet quality and health outcomes like obesity in developed countries. AFH eating is also emerging in low-income countries, but its influence on overall diet quality is under-researched. We examined the prevalence of AFH eating and its influence on the dietary patterns of Ugandan adults. Methods: This cross-sectional study employed a web-based survey to interview Ugandan adults aged 18 ~ 65 years. A qualitative food frequency questionnaire was used to assess the food group intake, which was then converted into daily intake frequencies. Principal component analysis was used to derive dietary patterns. The participants were then classified based on the tertiles (T) of dietary pattern scores. Results: About 75% of the 375 participants reported eating AFH. The young men, food insecure, and urban dwellers were more likely to eat AFH ≥ 5 times/week. Three dietary patterns emerged; the animal-based, beverage pattern; the high fat, sweet pattern; and the traditional, plant-based pattern. Participants who frequently ate AFH were 2.85 times and 5.64 times more likely to be in the second and third tertiles, respectively, of the animal-based, beverage pattern compared to the rare eaters (OR = 2.85, 95% CI: 1.35-6.06 for T2 vs T1; and OR = 5.64, 95% CI: 2.50-12.73 for T3 vs T1). The odds of being in the second tertile of the high fat, sweet pattern was significantly higher for frequent AFH eaters compared to the rare eaters (OR = 2.61, 95% CI:1.23-5.52). Conclusions: The prevalence of AFH eating was high. Frequent AFH eating was common among the young, male, food insecure, and urban dwellers, and was associated with unhealthy dietary patterns.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.147-158
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• 2009

Recently, the data model in stream data environment has massive, continuous, and infinity properties. However the stream data processing like query process or data analysis is conducted using a limited capacity of disk or memory. In these environment, the traditional frequent pattern discovery on transaction database can be performed because it is difficult to manage the information continuously whether a continuous stream data is the frequent item or not. In this paper, we propose the method which we are able to predict the frequent items using the regression model on continuous stream data environment. We can use as a prediction model on indefinite items by constructing the regression model on stream data. We will show that the proposed method is able to be efficiently used on stream data environment through a variety of experiments.

• Journal of Fashion Business
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• v.13 no.5
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• pp.121-134
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• 2009

The purpose of this study was to analyze the wearing pattern and design preference of shoes for men, and to develop the possibility and strategy of the shoes market for the shoes marketers and manufacturers. In this study, the data obtained from 285 respondents were analyzed by the descriptive statistics. The results from the data were as follows : The most frequent brand among the 45 shoes brand by 285 respondents described in free style was 'Esquire'. The 268 respondents possessed two shoes or more, the most frequent shoes' color was black, and the most preferred brand was 'Kumkang'. The 195 respondents indicated the discomfort of ready-made shoes, The 198 respondents discarded their shoes 'on the reason of worn-out', the 98 respondents indicated that the most important thing in the purchasing point was 'the comfort of shoes' The respondents preferred shoes with 'slip-on type', 'cow leather', 'semi-rounded toe', 'no-metal ornaments', 'moccasin tip', 'leather-sole', and '3cm heel'. Finally, this study proposed that the best strategy for shoes marketers and manufacturers was to upgrade the comfort of shoes by design(line) and the material with functional textures.