• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.31-36
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• 2021

Stress is a common risk factor for health and is associated with the endocrine gland and immune system. Studies on the association between stress and biochemical factors have been conducted worldwide, but studies on the association have been rare in South Korea. Therefore, the aims of this study are to analyze the relationship of stress with demographic information and biochemical factors for domestic adults and derive risk factors for stress. For data analysis, stress and normal groups were analyzed using binary logistic regression. In both men and women, age and average daily sleep time during the weekday were highly associated with stress, and the depression score (Patient Health Questionnaire-9) was also highly related with stress. In women, white blood cell levels were highly associated with stress, and in men, red blood cell levels were highly related with stress. These findings will contribute to the prevention of stress and the national health in the future.

• Proceedings of the Korean Society for Information Management Conference
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• 1998.08a
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• pp.243-246
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• 1998

텍스트 자동분석을 통해 얻어진 통계적인 용어연관성은 정보검색 및 언어 처리와 관련된 여러 분야에서 폭넓게 이용되고 있다. 용어연관성을 구하기 위한 연관계수는 여러 가지가 있지만 적용분야에 관계없이 유사계수 공식이나 상호정보량 공식이 주류를 차지하고 있다. 이런 공식들은 그 통계적 특성이 서로 다르기 때문에 알맞은 적용분야를 파악할 필요가 있다. 이 연구에서는 필요 연관계수 공식의 특성을 이론적으로 파악하였고, 실험으로 검증하기 위하여 240만 어절 분량의 실험용 한국어 신문기사 데이터베이스를 구축하였다.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.495-503
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• 2011

The most widely used data mining technique is to find association rules. Association rule mining is the method to quantify the relationship between each set of items in very huge database based on the association thresholds. There are some basic association thresholds to explore meaningful association rules ; support, confidence, lift, etc. Among them, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The net confidence and the attributably pure confidence were developed to compensate for this drawback, but they have other drawbacks.In this paper we consider some predictive similarity measures for binary data in cluster analysis and multi-dimensional analysis as association threshold to compensate for these drawbacks. The comparative studies with net confidence, attributably pure confidence, and some predictive similarity measures are shown by numerical example.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.727-736
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• 2014

The fundamental concerns of this paper are to analyze the effects of some characteristics on the employment of new college graduated students in viewpoint of data visualization. We use individual and department characteristic data of K-university graduated students in 2010. We apply multiple correspondence analysis, decision tree analysis, association rules and social network analysis for data visualization. The results of the analysis are summarized as follows. First, an analysis of the determinants of employment shows that GPA, department category, age and number of majors, recruiting time affect the employment rate. Second, higher GPA and natural category of department positively affect the employment rate. Finally, low age, single major and early recruiting time also positively affect the employment rate.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.203-216
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• 2022

In the recent business environment, purchase patterns are changing around the influence of COVID-19 and the online market. This study analyzed cluster and correlation analysis based on purchase and product information. The cluster analysis of new methods was attempted by creating customer, product, and cross-bonding clusters. The cross-bonding cluster analysis was performed based on the results of each cluster analysis. As a result of the correlation analysis, it was analyzed that more association rules were derived from a cross-bonding cluster, and the overlap rate was less. The cross-bonding cluster was found to be highly efficient. The cross-bonding cluster is the most suitable model for recommending products according to customer needs. The cross-bonding cluster model can save time and provide useful information to consumers. It is expected to bring positive effects such as increasing sales for the company.

• Journal of the Korean Society for information Management
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• v.21 no.4 s.54
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• pp.281-294
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• 2004

Association measures are applied to various applications, including information retrieval and data mining. Each association measure is subject to a close examination to its tendency to prefer high or low frequency level because it has a significant impact on the performance of applications. This paper examines the frequency level preference(FLP) tendency of some popular association measures using artificially generated cooccurrence data, and evaluates the results. After that, a method of how to adjust the FLP tendency of major association measures such as cosine coefficient is proposed. This method is tested on the cooccurrence-based query expansion in information retrieval and the result can be regarded as promising the usefulness of the method. Based on these results of analysis and experiment, implications for related disciplines are identified.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.413-418
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• 2020

Neuralgia is a disease that involves severe pain and has a very strong effect on the quality of human life, and the prevalence of the disease increases with aging. To date, previous studies on neuralgia were mainly focused on associations with mental illness, demographic information, and nutrients, and studies on association with blood information were very rare. Therefore, the objectives of this study are to examine the association between neuralgia and blood parameters and find clinical indicators related to neuralgia. To analyze the data, we used binary logistic regression based on data of the Korea National Health and Nutrition Examination Survey. Our results showed that age tended to have the higher association with neuralgia in both men and women, waist circumference and hematocrit level were associated with neuralgia in women, and fasting blood glucose and hemoglobin levels were associated with neuralgia in men. Also, we found that the association of neuralgia with waist circumference and blood information differed according to gender.

• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.129-137
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• 2002

최근 MarketPlace의 시장의 한 영역으로 형성되고 있는 전자상거래 사이버 쇼핑몰의 규모와 확산도를 알아보고, 이 중 종합쇼핑몰 3곳, 전문쇼핑몰 1곳의 사이트 현황을 살펴본다. 또한 교차판매를 위한 상품간 연관성분석과 소비자간 구성과 거래동향 등을 분석하기 위하여 Data Mining을 이용한다. 좀 더 세분화된 고객분석을 위한 개선점을 제기하고, 이를 통하여 전반적인 전자상거래 쇼핑몰에 대한 인식을 제고한다.

• The Korean Journal of Applied Statistics
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• v.12 no.1
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• pp.83-95
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• 1999

본 논문의 목적은 인터넷에서 범주형 자료분석에 대한 전문적인 지식이 없는 일반 분석자들에게 보다 쉽고, 간편하게 다룰 수 있는 범주형 자료 분석 시스템을 제공하는것이다. 이 분석 시스템은 크게 세 가지 측면으로 설계하여 구현하였다. 첫째, 범주형 자료에 대한 탐색적 자료분석을 위하여 세 가지 종류의 히스토그램을 제공한다. 둘째, 범주형 변수들간에 존재하는 연관성을 측정하기 위한 여러 연관성 측도들을 제공한다. 특히, 현재 많이 사용되는 통계 패키지들에서 제공하지 못하는 모자익 그림과 연관 그림을 동적 그래픽스로 구현하여 연관성을 측정하거나 모형을 설정하는데 유용한 정보를 얻을 수 있도록 하였다. 셋째, 대수선형모형에 대한 분석을 통해 사용자가 가장 잘 적합된 대수선형모형을 선택할 수 있게 하였다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.141-148
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• 2012

The advance of technologies for human genome makes it possible that the analysis of association between genetic variants and diseases and the application of the results to predict risk or susceptibility to them. Many of those studies carried out in case-control study. For quantitative traits, statistical analysis methods are applied to find single nucleotide polymorphisms (SNP) relevant to the diseases and consider them one by one. In this study, we presented methods to select informative single nucleotide polymorphisms and predict risk for quantitative traits and compared their performance. We adopted two SNP selection methods: one considering single SNP only and the other of all possible pairs of SNPs.