• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.57-64
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• 2009

This study has analysed actual overbreak, shotcrete rebound and the ratio between the actual quantity of shotcrete to designed shotcrete measured during a NATM tunnel construction. The measured shotcrete rebound was about 7.2% in average which was about half the allowable rebound (15%), showing shotcrete spraying was performed well. Based on the measurement of excavated tunnel shape, average overbreak was about 28.5cm after tunnel excavation by drill and blasting method. This was about 260% of allowable overbreak. In addition, due to the rebound and overbreak actual amount of shotcrete used in the tunnelling work was about 116.5 % of the designed value. According to the field measurement the ratio of actual shotcrete to designed value showed some relation with standard support pattern, but the size of overbreak did not show the correlation with standard support pattern. Hence current design specifications stating the size of overbreak based entirely on standard support pattern should perhaps be reestablished. The insight into the design guideline regarding overbreak and shotcrete.

• Journal of Korean Tunnelling and Underground Space Association
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• v.5 no.3
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• pp.241-250
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• 2003

Rock mass is very inhomogeneous in nature and data obtained by site investigations and tests are very limited. For this reason, many uncertainties are to be included in the process of constructing structures in rock mass. In the design of a tunnel, support pattern, advance rate, and excavation method, which are important design parameters, must be determined to be optimal. However, it is not easy to determine those parameters. Moreover if those parameters are determined incorrectly, unexpected risk occurs such as decrease in the stability of a tunnel or economic loss due to the excessive supports etc. In this study, how to determine an optimal support pattern and advance rate, which are the important tunnel design parameters, is introduced based on a risk analysis. It can be confirmed quantitatively that the more supported a tunnel is, the larger reliability index becomes and the more stable the tunnel becomes. Also an optimal support pattern and advance rate can be determined quantitatively by performing a risk analysis considering construction cost and the cost of loss which can be occurred due to the collapse of a tunnel.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.1
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• pp.1-8
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• 2002

The traditional approaches of association rule mining have relied on high support condition to find interesting rules. However, in some application such as analyzing the web page link and discovering some unusual combinations of some factors that have always caused some disease, we are interested in rules with high confidence that have very low support or need not have high support. In these cases, the traditional algorithms are not suitable since it relies on first satisfying high support. In this paper, we propose a new model, CP(Confidence Pattern)-Tree, to identify high confidence rule between 2-items without support constraint. constraint. In addition, we discuss confidence association rule between two more items without support constraint.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.16 no.2
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• pp.89-97
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• 2009

Purpose: To identify the health condition, home care support, support requirement of poor and elderly people living alone. Method: Data was collected through self-administered questionnaires and analyzed by descriptive statistics, t-test, ANOVA and correlation. Survey involved 269 conveniently selected who have a social support in H city. Result: Perceived health condition of subjects was bad to moderate (mean score: 2.22). There were significant home care support differences according to gender, religion, education level and dwelling pattern. Support requirement was influences only by the dwelling pattern. Perceived health condition showed a positive correlation with home care support of friends and neighbors, and a negative correlation with support requirement (medical, material, economic emotional support). Conclusion: These findings are expected to make a positive contribution to create an ideal intervention for public visiting nurses and social workers to improve the quality of life in poor and elderly people who live alone.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.77-83
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• 2015

Frequent pattern mining, which is one of the major areas actively studied in data mining, is a method for extracting useful pattern information hidden from large data sets or databases. Moreover, frequent pattern mining approaches have been actively employed in a variety of application fields because the results obtained from them can allow us to analyze various, important characteristics within databases more easily and automatically. However, traditional frequent pattern mining methods, which simply extract all of the possible frequent patterns such that each of their support values is not smaller than a user-given minimum support threshold, have the following problems. First, traditional approaches have to generate a numerous number of patterns according to the features of a given database and the degree of threshold settings, and the number can also increase in geometrical progression. In addition, such works also cause waste of runtime and memory resources. Furthermore, the pattern results excessively generated from the methods also lead to troubles of pattern analysis for the mining results. In order to solve such issues of previous traditional frequent pattern mining approaches, the concept of representative pattern mining and its various related works have been proposed. In contrast to the traditional ones that find all the possible frequent patterns from databases, representative pattern mining approaches selectively extract a smaller number of patterns that represent general frequent patterns. In this paper, we describe details and characteristics of pattern condensing techniques that consider the maximality or closure property of generated frequent patterns, and conduct comparison and analysis for the techniques. Given a frequent pattern, satisfying the maximality for the pattern signifies that all of the possible super sets of the pattern must have smaller support values than a user-specific minimum support threshold; meanwhile, satisfying the closure property for the pattern means that there is no superset of which the support is equal to that of the pattern with respect to all the possible super sets. By mining maximal frequent patterns or closed frequent ones, we can achieve effective pattern compression and also perform mining operations with much smaller time and space resources. In addition, compressed patterns can be converted into the original frequent pattern forms again if necessary; especially, the closed frequent pattern notation has the ability to convert representative patterns into the original ones again without any information loss. That is, we can obtain a complete set of original frequent patterns from closed frequent ones. Although the maximal frequent pattern notation does not guarantee a complete recovery rate in the process of pattern conversion, it has an advantage that can extract a smaller number of representative patterns more quickly compared to the closed frequent pattern notation. In this paper, we show the performance results and characteristics of the aforementioned techniques in terms of pattern generation, runtime, and memory usage by conducting performance evaluation with respect to various real data sets collected from the real world. For more exact comparison, we also employ the algorithms implementing these techniques on the same platform and Implementation level.

• Journal of muscle and joint health
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• v.9 no.2
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• pp.135-143
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• 2002

Exercise is an important strategy for health promotion in patients having osteoarthritis. But, lots of patients with osteoarthritis were underexercised. Exercise pattern and influencing factors of exercise barrier are not well-known. To address this issue, we studied the exercise pattern and influencing factors of exercise barrier in patients with osteoarthritis. The subjects of the study were 463 adult osteoarthritis (Mean age = 61.63 years) who had diagnosed osteoarthritis by rheumatologist. Data were gathered from May 1999 to February 2000 using a questionnaire and exercise barrier(Sallis et al, 1989), exercise pattern(Lee et al., 2000), physical status by WOMAC(Bellamy, 1989), socail support(Sallis et al., 1989), fatigue and pain using graphic rating scale, depression by CES-D(Radloff,1977). Data were analyzed with the SPSS win 6.0 using frequency, ANOVA, Stepwise multiple regression. The results of this study were as follows; 1) 56.4% of sample was 'do not exercise at all', 'longer rest than exercise', was 15.9%, 'longer exercise than rest' was 7.2%, 'exercise regularly' was 20.5%. 2) Social support (F=10.349, p=0.000) and exercise barrier(F=4.455, p=0.004) were showed significantly difference by exercise pattern. 3) Influencing factors of exercise barrier were depression and social support. Thoses explained 13.3% of exercise barrier. In conclusion, half of osteoarthritis patient did not do exercise and it was shown that depression and social support were major influencing factors to exercise barrier. The results of this study can be applied to develop the health promoting educational program for patients with osteoarthritis.

• Journal of the Korean Home Economics Association
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• v.47 no.5
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• pp.59-66
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• 2009

The present study identified the patterns of support exchange between the elderly living alone and their children in a metropolitan city in Japan and examined the relationships between the patterns of support exchange and their life satisfaction. The sample was collected from 1,020 the elderly living alone in Osaka city selected with random sampling method. The questionnaires were mailed to the respondents, who were asked to send them back. The response rate was 51.7%(n = 526). We used 371 sample of respondents who have children. The results indicated that:(1)the patterns of support exchange were different by the types of social support, (2)the life satisfaction of the elderly who had an appropriate balance of a support exchange pattern was significantly higher than the life satisfaction of those who had low support exchange or only provided supports to their children. The findings imply that an appropriate balance of support exchange between the elderly living alone and their children was crucial in improving life satisfaction of the elderly living alone.

• Journal of Digital Contents Society
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• v.13 no.2
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• pp.189-195
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• 2012

In this paper, face detection method in NIR(Near-InfraRed) images for human recognition is proposed. Edge histogram based on edge intensity and its direction, has been used to detect effectively faces on NIR image. The edge histogram descripts and discriminates face effectively because it is strong in environment of lighting change. SVM(Support Vector Machine) has been used as a classifier to detect face and the proposed method showed better performance with smaller features than in ULBP(Uniform Local Binary Pattern) based method.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.125-132
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• 2014

Since frequent pattern mining was proposed in order to search for hidden, useful pattern information from large-scale databases, various types of mining approaches and applications have been researched. Especially, frequent graph pattern mining was suggested to effectively deal with recent data that have been complicated continually, and a variety of efficient graph mining algorithms have been studied. Graph patterns obtained from graph databases have their own importance and characteristics different from one another according to the elements composing them and their lengths. However, traditional frequent graph pattern mining approaches have the limitations that do not consider such problems. That is, the existing methods consider only one minimum support threshold regardless of the lengths of graph patterns extracted from their mining operations and do not use any of the patterns' weight factors; therefore, a large number of actually useless graph patterns may be generated. Small graph patterns with a few vertices and edges tend to be interesting when their weighted supports are relatively high, while large ones with many elements can be useful even if their weighted supports are relatively low. For this reason, we propose a weight-based frequent graph pattern mining algorithm considering length-decreasing support constraints. Comprehensive experimental results provided in this paper show that the proposed method guarantees more outstanding performance compared to a state-of-the-art graph mining algorithm in terms of pattern generation, runtime, and memory usage.