• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.95-99
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• 2010

Spatial information systems have developed in order to provide users with customized land information in u-City environments. The spatial information systems can detect spatial information for users anytime anywhere. Information which is analyzed by data mining techniques can be offered for other users. Therefore, we propose open map API-based prototype which utilizes frequent pattern mining technique. Proposed prototype can mine interesting trip routes and unknown attractions in location data of geophoto. Also, proposed prototype is the first attempt which analyzes spatial patterns can be represented on a map which is selected by users. Our prototype can be applied to the smart phone like mobile devices.

• Journal of Korean Academy of Nursing
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• v.25 no.3
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• pp.457-471
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• 1995

This research was done to promote improvement of practical application of nursing diagnoses and to improve the quality of nursing. The subjects of this research were 156 second year students of C junior nursing college who were giving adult patient care. The nursing diagnoses of 312 reports were analyzed using NANDA. In these case reports only nursing diagnoses were considered, of which there were a total of 982. In the data analysis the 9H of the nursing students' nursing diagnoses matched with 105 NANDA nursing diagnoses, Of these, the most frequent diagnoses were pain(165, 17.48%), anxiety(101, 10.70%), alteration in nutrition(83, 8.79%) , sleep disturbance (67, 7.10%), in activity intolerance (67, 7.10%), ineffective breathing pattern(51,5.40%). The etiology for the students' nursing diagnoses were compared with NANDA's nursing diagnoses by frequency. The most frequent etiology for the nursing diagnoses of pain was a biological etiology(50, 31%), for anxiety, situation crisis(58, 57.43%), for alteration in nutrition, indigesion(23, 27.71%), for sleep disturbance, external etiology(25, 37.32%), for activity intolerance, immobile position(22, 32.84%), for ineffective breathing pattern, pain(35, 68.63%), and for ,impaired physical mobility, pain(31, 65.96%). The most frequent etiology for constipation was inadquate digestion of water and cellulose (16, 34.78%), for fluid volume felicity, loss of body fluid (21, 52.50%), for impaired skin integrity, external etilogy(16, 43.24%), for impaired physical mobility, pain(22, 62.86%) , for knowledge deficits, cognition disturbance(9, 27.27％), for ineffective air way clearance, secretion obstruction(14, 48.27%) , for impaired gas exchange, loss of transport ability of blood oxygen(9, 37.50%) , and for powerlessness, therapy environment (5, 22.73%). The number of nursing diagnoses by pattern was exchange(16), moving(6), feeling(4), choosing(4), relating(3), communication(1), perceiving(1), knowing(1), valuing(1).

• Korean Journal of Community Nutrition
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• v.21 no.2
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• pp.140-151
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• 2016

Objectives: This study was performed to examine the consumption patterns of convenience food at convenience stores, dietary habits, and perception as well as knowledge of food additives among university students. Methods: Subjects were 352 university students in Cheongju, Korea, and data was collected by a self-administered questionnaire. They were divided into three groups according to the frequency of consumption of convenience food at convenience stores: 79 rare (${\leq}1$ time/month), 89 moderate (2-4 times/month) and 184 frequent (${\geq}2$ times/week). Results: More subjects from the frequent consumption group lived apart from parents (p<0.001) and possessed more pocket money (p<0.01). Frequent consumption group consumed noodles, Kimbab, and sandwich & burger significantly more often (p<0.001, respectively) than others. In addition, frequent consumption of convenience foods at convenience stores was associated with frequent breakfast skipping (p<0.05), irregular meal time (p<0.01), snacking (p<0.05), and eating late night meal (p<0.001). More from the rare consumption group had heard about food additives previously compared to the frequent consumption group (79.7% vs. 63.6%, p<0.01). Frequent consumption group showed significantly higher score than did the rare consumption group for the following questions: monosodium glutamate is harmful to your health (p<0.05), food additives are necessary for food manufacturing (p<0.005), food additives need to be labeled on products (p<0.05), there is no food additive at all if labeled as no preservatives, no coloring, and no added sugar (p<0.05). There was a significant difference in degrees of choosing products with less food additives depending on the consumption pattern. Conclusions: Our results provided a better understanding of the factors associated with frequent consumption of convenience foods at convenience stores among university students and will be useful to develop a nutrition education program for those who are more prone to consume convenience foods.

• The KIPS Transactions:PartD
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• v.19D no.2
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• pp.139-146
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• 2012

Data Mining is widely used to discover knowledge in many fields. Although there are many methods to discover association rule, most of them are based on frequency-based approaches. Therefore it is not appropriate for stream environment. Because the stream environment has a property that event data are generated continuously. it is expensive to store all data. In this paper, we propose a new method to discover association rules based on stream environment. Our new method is using a variable window for extracting data items. Variable windows have variable size according to the gap of same target event. Our method extracts data using COBJ(Count object) calculation method. FPMDSTN(Frequent pattern Mining over Data Stream using Terminal Node) discovers association rules from the extracted data items. Through experiment, our method is more efficient to apply stream environment than conventional methods.

• Journal of Korea Multimedia Society
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• v.8 no.9
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• pp.1163-1176
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• 2005

The Web is the largest distributed information space but, the individual's capacity to read and digest contents is essentially fixed. In these Web environments, mining traversal patterns is an important problem in Web mining with a host of application domains including system design and information services. Conventional traversal pattern mining systems use the inter-pages association in sessions with only a very restricted mechanism (based on vector or matrix) for generating frequent K-Pagesets. We extend a family of novel algorithms (termed WebPR - Web Page Recommend) for mining frequent traversal patterns and then pageset to recommend. We add a WebPR(A) algorithm into a family of WebPR algorithms, and propose a new winWebPR(T) algorithm introducing a window concept on WebPR(T). Including two extended algorithms, our experimentation with two real data sets, including LadyAsiana and KBS media server site, clearly validates that our method outperforms conventional methods.

• Journal of the Korea Fashion and Costume Design Association
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• v.11 no.1
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• pp.155-167
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• 2009

This study reviewed the basic principle of "Maximalism" fashion and analyzed the properties of fashion types with found the frequencies of various styles and design factors of "Maximalism" fashion in collection. The limits of this study is from 2001, s/s, maximalism was embossed in a modem fashion, to 2007, f/w, and collected the fashion collection pictures from www.samsungdesign.net and www.style.com. The results of this study are as follows. First, the most frequent style of "Maximalism" design showed in International fashion collection was "exaggerated style." Second, the design factors of "Maximalism" fashion were also examined. In case of silhouette, "hourglass silhouette" was the most frequent silhouette. In case of pattern, "solid" color was the most frequently used. In case of materials, the soft materials were the most frequently used. Third, the frequencies of design factors of maximalism by presentation types was compared and analyzed. In case of expansion, "bulk silhouette" was the most frequent silhouette to be appeared, and about multi-ethnic, futurelism, and elegance, "hourglass silhouette" was frequent appeared. In case of pattern, "solid" color was the most frequently used in all types. In case of materials, the hard materials were the most frequently used in expansion, multi-ethnic and futurelism. "Maximalism" fashion which is most splendid trend would grow up rapidly in the modem fashion market and influence on the other fashion trend in our every day life. Consequently, this research can be referred as practical information in fashion marketing and it will contribute to the future fashion research as well.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.2
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• pp.81-94
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• 2005

Recently, wireless LAN circumstance is being widely deployed in Public spots. Many People use Portable equipments such as PDA and laptop computer for multimedia applications, and also demand of mobility support is increasing. However, handoff latency is inevitably occurred between both APs when clients move from one AP to another. To reduce handoff latency. in this paper, we suggest WFH(Weighted Frequent Handoff) using effective data structure. WFH improves cache hit ratio using a new cache replacement algorithm considering the movement pattern of users. It also reduces unessential duplicate traffics. Our algorithm uses FHR(Frequent Handoff Region) that can change pre-authentication lesion according to QoS based user level, movement Pattern and Neighbor Graph that dynamically captures network movement topology.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.65-78
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• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.741-754
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• 2008

Due to the characteristics of a data stream, it is very important to confine the memory usage of a data mining process regardless of the amount of information generated in the data stream. For this purpose, this paper proposes the Prime pattern tree(PPT) for finding frequent itemsets over data streams with using the confined memory space. Unlike a prefix tree, a node of a PPT can maintain the information necessary to estimate the current supports of several itemsets together. The length of items in a prime pattern can be reduced the total number of nodes and controlled by split_delta $S_{\delta}$. The size and the accuracy of the PPT is determined by $S_{\delta}$. The accuracy is better as the value of $S_{\delta}$ is smaller since the value of $S_{\delta}$ is large, many itemsets are estimated their frequencies. So it is important to consider trade-off between the size of a PPT and the accuracy of the mining result. Based on this characteristic, the size and the accuracy of the PPT can be flexibly controlled by merging or splitting nodes in a mining process. For finding all frequent itemsets over the data stream, this paper proposes a PPT to replace the role of a prefix tree in the estDec method which was proposed as a previous work. It is efficient to optimize the memory usage for finding frequent itemsets over a data stream in confined memory space. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.