• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.3996-4004
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• 2022

The data collected by the In-Situ Decommissioning (ISD) sensors are time-specific, age-specific, and developmental stage-specific. Research has been done on the stream data collected by ISD testbed in the recent few years to seek both frequent episodes and abnormal frequent episodes. Frequent episodes in the data stream have confirmed the daily cycle of the sensor responses and established sequences of different types of sensors, which was verified by the experimental setup of the ISD Sensor Network Test Bed. However, the discovery of abnormal frequent episodes remained a challenge because these abnormal frequent episodes are very small signals and may be buried in the background noise of voltage and current changes. In this work, we proposed Advanced Data Analytics (ADA) methods that are applied to the baseline data to identify frequent episodes and extended our approach by adding more features extracted from the baseline data to discover abnormal frequent episodes, which may lead to the early indicators of ISD system failures. In the study, we have evaluated our approach using the baseline data, and the performance evaluation results show that our approach is able to discover frequent episodes as well as abnormal frequent episodes conveniently.

• Journal of Physiology & Pathology in Korean Medicine
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• v.19 no.1
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• pp.38-43
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• 2005

The following conclusions are drawn from the review on the frequent urination of old people in perspective of Hyungsang medicine: Frequent urination is a difficulty in urination that is often common to old people. Frequent urination is one of the symptoms occurred when the nine body orifices do not operate normally because Jung(精) and Blood(血) are exhausted with ages. Frequent urination is brought by the deficiency of kidney, bladder and lung's Ki. In six meridian type persons, Yangmyung meridian type persons are most often afflicted with frequent urination because earth checks water(土克水). Persons with big cheek bones are easy to be caught by frequent urination due to the consumption of Jung(精) and Blood(血). Frequent urination is the source of geriatric diseases so that it should be treated preferentially. Frequent urination of old people should be treated with tonic prescription(補劑).

• The Journal of Korean Association of Computer Education
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• v.6 no.1
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• pp.1-8
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• 2003

We introduce a efficient vertical mining algorithm that reduces searching complexity for frequent k-itemsets significantly. This method includes sorting items by their LSI(Least Support Itemsets) similarity and then searching frequent itemsets in tree-based manner. The search tree structure provides several useful heuristics and therefore, reduces search space significantly at early stages. Experimental results on various data sets shows that the proposed algorithm improves searching performance compared to other algorithms, especially for a database having long pattern.

• Journal of Industrial Technology
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• v.25 no.A
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• pp.163-173
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• 2005

Recent sequential pattern mining algorithms mine all of the frequent sequences satisfying a minimum support threshold in a large database. However, when a frequent sequence becomes very long, such mining will generate an explosive number of frequent sequence, which is prohibitively expensive in time. In this paper, we proposed a novel sequential pattern algorithm using only closed frequent sequences which are small subset of very large frequent sequences. Our algorithm extends the sequence by depth-first search strategy with effective pruning. Using bitmap representation of underlying databases, we can obtain a closed frequent sequence considerably faster than the currently reported methods.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.623-634
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• 2016

Most of existing frequent pattern mining methods address time efficiency and greatly rely on the primary memory. However, in the era of big data, the size of real-world databases to mined is exponentially increasing, and hence the primary memory is not sufficient enough to mine for frequent patterns from large real-world data sets. To solve this problem, there are some researches for frequent pattern mining method based on disk, but the processing time compared to the memory based methods took very time consuming. There are some researches to improve scalability of frequent pattern mining, but their processes are very time consuming compare to the memory based methods. In this paper, we present PPFP as a novel disk-based approach for mining frequent itemset from big data; and hence we reduced the main memory size bottleneck. PPFP algorithm is based on FP-growth method which is one of the most popular and efficient frequent pattern mining approaches. The mining with PPFP consists of two setps. (1) Constructing an IFP-tree: After construct FP-tree, we assign index number for each node in FP-tree with novel index numbering method, and then insert the indexed FP-tree (IFP-tree) into disk as IFP-table. (2) Mining frequent patterns with PPFP: Mine frequent patterns by expending patterns using stack based PUSH-POP method (PPFP method). Through this new approach, by using a very small amount of memory for recursive and time consuming operation in mining process, we improved the scalability and time efficiency of the frequent pattern mining. And the reported test results demonstrate them.

• Culinary science and hospitality research
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• v.9 no.1
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• pp.65-80
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• 2003

The purpose of this study was to identify the college students'frequent usage groups of Western style restaurant in Ansan city. 200 samples among subjects were utilized for the analysis, and 150 samples were reserved far validating the discriminant function. Crosstabs, reliability analysis, stepwise discriminant analysis, and anova analysis were used for this study. The findings from this study were as follows. First, the result suggested that the four variables were important in discriminating the frequent usage group. Second, the result suggested that each discriminating variable between frequent usage groups was different significantly. Third, the result suggested that each usage situation between frequent usage groups was different significantly. Finally the study indicated the implications that could be provided some insight into the types of marketing strategies that can be successfully used by operators who manage Western style restaurants.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.3
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• pp.242-252
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• 2006

Data mining is the exploration and analysis of huge amounts of data to discover meaningful patterns. One of the most important data mining problems is association rule mining. Recent studies of mining association rules have proposed a closure mechanism. It is no longer necessary to mine the set of all of the frequent itemsets and their association rules. Rather, it is sufficient to mine the frequent closed itemsets and their corresponding rules. In the past, a number of algorithms for mining frequent closed itemsets have been based on items. In this paper, we use the transaction itself for mining frequent closed itemsets. An efficient algorithm is proposed that is based on a link structure between transactions. Our experimental results show that our algorithm is faster than previously proposed methods. Furthermore, our approach is significantly more efficient for dense databases.

• Journal of Information Processing Systems
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• v.6 no.1
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• pp.79-90
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• 2010

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams. Moreover, we propose a novel tree structure, called the Weighted Support FP-Tree (WSFP-Tree), that stores compressed crucial information about frequent itemsets. Empirical results show that our algorithm outperforms comparative algorithms under the windowed streaming model.

• The KIPS Transactions:PartD
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• v.18D no.3
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• pp.169-178
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• 2011

Recently, data sharing between enterprises or organizations is required matter for task cooperation. In this process, when the enterprise opens its database to the affiliates, it can be occurred to problem leaked sensitive information. To resolve this problem it is needed to hide sensitive information from the database. Previous research hiding sensitive information applied different heuristic algorithms to maintain quality of the database. But there have been few studies analyzing the effects on the items modified during the hiding process and trying to minimize the hided items. This paper suggests eFP-Tree(Extended Frequent Pattern Tree) based FP-Tree(Frequent Pattern Tree) to hide sensitive frequent itemsets. Node formation of eFP-Tree uses border to minimize impacts of non sensitive frequent itemsets in hiding process, by organizing all transaction, sensitive and border information differently to before. As a result to apply eFP-Tree to the example transaction database, the lost items were less than 10%, proving it is more effective than the existing algorithm and maintain the quality of database to the optimal.

• Nutrition Research and Practice
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• v.16 no.3
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• pp.392-404
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• 2022

BACKGROUND/OBJECTIVES: Numerous studies have examined the relationship between drinking behaviors and metabolic syndrome (MetS) for adults, but these include very few studies for young adults. This study therefore undertook to investigate the association between drinking behaviors and components of MetS among adult drinkers aged 20-30 years. SUBJECTS/METHODS: Using the 2016-2018 Korea National Health and Nutrition Examination Survey data, drinking behaviors of adults in the age group 20-30 years were divided into 4 groups: 1) group A, good drinking habits; 2) group B, frequent binge drinking but not frequent drinking; 3) group C, frequent drinking but not frequent binge drinking; 4) group D, frequent drinking and binge drinking. The association between MetS components and drinking behaviors was analyzed by applying multiple logistic regression analysis. RESULTS: We determined the prevalence risk compared to group A. In men, the prevalence risk of high triglyceride (TG) increased 2.051-fold in group C and 1.965-fold in group D. Moreover, in group D, the prevalence risk of low high density lipoprotein cholesterol (HDL-C) increased 0.668-fold, high blood pressure (BP) increased 2.147-fold, and MetS increased 1.567-fold. In women, there was an increased prevalence risk of low HDL-C (0.353-fold) and MetS (3.438-fold) in group C, whereas group D showed increased prevalence risk of abdominal obesity (2.959-fold), high TG (1.824-fold, and low HDL-C (0.424-fold). CONCLUSIONS: Our study indicates that frequent drinking increases the risk of high TG, whereas frequent and binge drinking increases the risk of high TG, low HDL-C, high BP, and prevalence of MetS in men. In women, frequent drinking without binge drinking increases the risk of low HDL-C and MetS, whereas frequent and binge drinking increases the risk of abdominal obesity, high TG, and low HDL-C. We propose that improvements in the drinking behaviors can reduce the prevalence of MetS.