• Culinary science and hospitality research
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• 제9권1호
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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.

• International Journal of Contents
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• 제3권2호
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• pp.18-24
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• 2007

Biological sequences such as DNA and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of more than hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological datasets with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with a fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. The experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

• Journal of the Korea Society of Computer and Information
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• 제12권4호
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• pp.179-189
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• 2007

Every purchase a customer makes builds patterns about how products are purchased together. The process of finding these patterns, called market basket analysis, is composed of two steps in the Microsoft Association Algorithm. The first step is to find frequent item-sets. The second step which requires much less time than the first step does is to generate association rules based on frequent item-sets. Even though the first step, finding frequent item-sets, is the core part of market basket analysis, when applied to Online Analytical Processing(OLAP) cubes it always raises several points such as longitudinal analysis becomes impossible and many unpractical transactions are built up. In this paper, a new OLAP cube structures designing method which makes longitudinal analysis be possible and also makes only real customers' purchase patterns be identified is proposed for market basket analysis.

• Journal of Korean Institute of Industrial Engineers
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• 제32권3호
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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 Internet Computing and Services
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• 제16권2호
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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.

• Korean Institute of Interior Design Journal
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• 제25권1호
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• pp.181-191
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• 2016

The purpose of this study is to analyze affordance of façade design by extracting analysis elements of affordance in façade design domestic five-star hotels. The subjects of this study were 88 domestic five-star hotels; we selected literature review and case study as methodology. The analysis elements included mass forms, exterior colors, window frame patterns, exterior characteristics, and door types based on the visual perception factors of façade design from the concept of affordance. The results of the study were as follows. First, the mass forms were divided into stackable, integral, and connection types, and stackable type was most frequent. Second, exterior colors were divided into achromatic, red, yellow, and opaque color parts, and the achromatic parts were most frequent. Third, the window frame patterns were divided into grid, irregular, full, horizontal, and vertical windows, and grid windows were most frequent. Fourth, the exterior characteristics were divided into formative mass, horizontal partition, design contrast, tiled roof, and facility connection, and the formative mass was most frequent. Fifth, the door types were divided into porch, piloti, and wall type, and porch was most frequent. The results of this study showed that the façade design elements such as mass forms, exterior colors, window frame patterns, exterior characteristics, and door types intentionally contained affordance.

• Journal of Information Technology Applications and Management
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• 제26권1호
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• pp.65-75
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• 2019

As the use of semantic web based on XML increases in the field of data management, a lot of studies to extract useful information from the data stored in ontology have been tried based on association rule mining. Ontology data is advantageous in that data can be freely expressed because it has a flexible and scalable structure unlike a conventional database having a predefined structure. On the contrary, it is difficult to find frequent patterns in a uniformized analysis method. The goal of this study is to provide a basis for extracting useful knowledge from ontology by searching for frequently occurring subgraph patterns by applying transaction-based graph mining techniques to ontology schema graph data and instance graph data constituting ontology. In order to overcome the structural limitations of the existing ontology mining, the frequent pattern search methodology in this study uses the methodology used in graph mining to apply the frequent pattern in the graph data structure to the ontology by applying iterative node chunking method. Our suggested methodology will play an important role in knowledge extraction.

• Child Health Nursing Research
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• 제22권4호
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• pp.370-378
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• 2016

Purpose: The purpose of this study was to examine current status of somatic symptoms of late school-aged children using the school health clinic and to investigate the influence of perceived attachment security and social support on their somatic symptoms. Methods: For this descriptive study, self-report questionnaires were completed by fifth and sixth graders attending 'A' elementary school in Gyeonggi-do. Data from 216 students were included. Data analysis included descriptive statistics, t-test, ANOVA, Pearson correlation coefficient, and multiple regression analysis. Results: Most frequent somatic symptoms were headache, fainting, backache, numbness in a body part, and muscle ache in that order. More frequent somatic symptoms were reported by girls, students who recognized their family SES as low, students who used school health clinic often and students who were dissatisfied with school life. Somatic symptom showed negative correlations with attachment stability and perceived social support from family and teachers. In the regression analysis, the variables; low attachment stability, female gender, and low satisfaction with school affected more frequent somatic symptoms. Conclusion: Careful monitoring of late school-aged children expressing frequent somatic symptoms is required. Intervention programs to improve attachment security and satisfaction with school should be developed for school children, especially girls, presenting with somatic symptoms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3221-3242
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• 2021

The purpose of the study is to examine the current study trend of 'QR code' and suggest a direction for the future study of big data analysis: (1) Background: study trend of 'QR code' and analysis of the text by subject field and year; (2) Methodology: data scraping and collection, EXCEL summary, and preprocess and big data analysis by R x 64 4.0.2 program package; (3) the findings: first, the trend showed a continuous increase in 'QR code' studies in general and the findings were applied in various fields. Second, the analysis of frequent keywords showed somewhat different results by subject field and year, but the overall results were similar. Third, the visualization of the frequent keywords also showed similar results as that of frequent keyword analysis; and (4) the conclusions: in general, 'QR code' studies are used in various fields, and the trend is likely to increase in the future as well. And the findings of this study are a reflection that 'QR code' is an aspect of our social and cultural phenomena, so that it is necessary to think that 'QR code' is a tool and an application of information. An expansion of the scope of the analysis is expected to show us more meaningful indications on 'QR code' study trends and development potential.

• Journal of the Korean Home Economics Association
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• 제41권12호
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• pp.245-255
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• 2003

This study examined young children's metaplay. 84 preschool subjects were videotaped as they engaged in pretend play with the same-aged peers. Dyads were identified as engaging in frequent or infrequent pretend play. Results showed that frequent pretend play dyads more engaged in request for clarification, and persuading than infrequent pretend dyads. Frequent pretend dvads were more likely to use disagreeing with reason and extending. It was concluded that developmentally useful interactions transpire outside of the pretend frame.