• Proceedings of the Korean Society for Information Management Conference
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• 2004.08a
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• pp.17-22
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• 2004

연관성 척도값은 연관성 분석 대상이 고빈도인지 저빈도인지 여부에 따른 영향을 받는데, 연관성 척도마다 주로 높은 연관성으로 판정하는 대상의 빈도수준이 다양하게 나타난다. 이런 연관성 척도의 빈도수준 선호경향을 수치로 나타낼 수 있다면 연관성 척도를 사용하는 실험이나 분석에서 시행착오나 시간낭비를 줄일 수 있을 것이다. 이를 위해서 연관성 척도의 빈도수준 선호지수(FLPI)를 개발하였다. 개발된 빈도수준 선호지수는 연관성 척도와 출현빈도 사이의 상관성을 이용하는 것으로서 연관성 척도를 적용하는 실험이나 분석의 효율을 높이는데 기여할 것으로 기대된다.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1345-1352
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• 2014

Data mining is the representative methodology to obtain meaningful information in the era of big data.By Wikipedia, association rule learning is a popular and well researched method for discovering interesting relationship between itemsets in large databases using association thresholds. It is intended to identify strong rules discovered in databases using different interestingness measures. Unlike general association rule, inverse association rule mining finds the rules that a special item does not occur if an item does not occur. If two types of association rule can be simultaneously considered, we can obtain the marketing information for some related products as well as the information of specific product marketing. In this paper, we propose a balanced attributable relative accuracy applicable to these association rule techniques, and then check the three conditions of interestingness measures by Piatetsky-Shapiro (1991). The comparative studies with rule accuracy, relative accuracy, attributable relative accuracy, and balanced attributable relative accuracy are shown by numerical example. The results show that balanced attributable relative accuracy is better than any other accuracy measures.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.385-387
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• 2004

현재의 침입탐지 시스템의 문제점들을 개선하기 위해 침입탐지 정보의 축약기술 및 연관성 분석 기법들에 대한 연구들이 진행 중이다. 또한 최근에는 침입탐지 정보의 연관성 분석 시스템에 대한 효과성 검증에 대한 연구도 진행 중이다. 본 논문에서는 침입탐지 정보의 축약기술 및 연관성 분석 시스템의 효과성을 검증하기 위한 평가방안을 제안하였다 즉, 침입탐지 정보의 연관성 분석 시스템에 필요한 기능 요구사항을 제시하고, 그러한 기능을 객관적으로 평가할 수 있는 방법으로 가중치 및 행렬에 의한 방법을 제안하였다.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.397-405
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• 2010

Data mining is the method to find useful information for large amounts of data in database, and one of the important goals is to search and decide the association for several variables. The task of association rule mining is to find certain association relationships among a set of data items in a database. There are three primary measures for association rule, support and confidence and lift. In this paper we developed a association rule ranking function by decreased lift influence to generate association rule for items satisfying at least one of three criteria. We compared our function with the functions suggested by Park (2010), and Wu et al. (2004) using some numerical examples. As the result, we knew that our decision function was better than the function of Park's and Wu's functions because our function had a value between -1 and 1regardless of the range for three association thresholds. Our function had the value of 1 if all of three association measures were greater than their thresholds and had the value of -1 if all of three measures were smaller than the thresholds.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.709-717
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• 2010

The task of association rule mining is to find certain association relationships among a set of data items in a database. There are three primary measures for association rule, support and confidence and lift. In this paper we developed a association rule ranking function using conditional probability increment ratio. We compared our function with several association rule ranking functions by some numerical examples. As the result, we knew that our decision function was better than the existing functions. The reasons were that the proposed function of the reference value is not affected by a particular association threshold, and our function had a value between -1 and 1 regardless of the range for three association thresholds. And we knew that the ranking function using conditional probability increment ratio was very well reflected in the difference between association rule measures and the minimum association rule thresholds, respectively.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.351-355
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• 2007

문서 여과 시스템은 사용자의 정보요구를 기준으로 문서들을 선별하여 제시한다. 사용자의 정보요구는 하나 이상의 단어들로 구성된 프로파일로 표현이 되며, 문서의 여과 과정 동안에 발생하는 사용자의 연관성 평가를 통해 구체적인 내용으로 변할 수 있다. 기존 연구의 경우 사용자는 자신이 직접 연관성 평가에 참여하여 평가 정보를 입력하고, 사용자가 평가한 긍정적 피드백 정보를 이용하여 사용자 프로파일을 학습한다. 본 연구는 사용자가 평가한 긍정적 연관성 피드백 뿐만 아니라 부정적 연관성 피드백을 함께 이용한 사용자 프로파일 학습 방법을 제안한다. 제안된 방법과, 대표적인 연관성 피드백 방법인 Rocchio 방법과의 성능을 측정하기 위해 네 가지 토픽에 대하여 여과를 수행하였다. 실험한 결과 부정적 연관성 피드백 정보를 이용하였을 경우 Rocchio 방법 보다는 6% 더 성능이 높은 것을 볼 수 있었다. 실험결과 부정적 평가를 받은 문서를 이용하여 사용자가 선호하지 않는 문서를 제거함으로써 여과 시스템의 성능을 향상 시킬 수 있었다.

• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.117-123
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• 2015

본 연구에서는 DFT를 이용하여 Nucleophilicity와 Basicity의 연관성에 대한 계산화학적 분석을 수행하였다. Basicity는 선정된 모델 분자의 protonation 반응에서 생성물과 반응물의 enthalpy 변화량인 양성자 친화도(Proton affinity, PA) 값을 구하여 분석하였다. 계산한 결과는 실험을 통해 얻은 PA 결과와 경향성이 거의 일치함을 확인하였다. Nucleophilicity는 모델 분자들과 $CH_3Br$ (electrophile)의 $SN_2$반응에서 gibbs free energy of activation(${\Delta}G^{\ddag}$) 값으로 그 경향성을 분석하였다. 또한 용매의 종류를 다르게 하여 용매에 따른 ${\Delta}G^{\ddag}$ 값의 경향성도 확인하였다. 각 용매에 따라 구한 ${\Delta}G^{\ddag}$와 PA의 상관관계를 비교하였으나, 큰 연관성은 보이지 않았다. 이에 ${\Delta}G^{\ddag}$와 PA의 상관관계를 보여줄 수 있는 parameter를 찾기 위하여 각 모델 분자의 Electronegativity와 Polarizability를 계산하여 연관성을 비교해보았다. Polarizability를 적용했을 때 Nucleophilicity와 Basicity사이의 연관성을 나타낼 수 없었던 반면, Electronegativity를 적용하여 Basicity와 Nucleophilicity의 연관성 보일 수 있음을 이론적으로 규명하였다.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.179-188
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• 2011

Association rule represents the relationship between items in a massive database by quantifying their relationship, and is used most frequently in data mining techniques. In general, association rule technique generates the rule, 'If A, then B.', whereas negative association rule technique generates the rule, 'If A, then not B.', or 'If not A, then B.'. We can determine whether we promote other products in addition to promote its products only if we add negative association rules to existing association rules. In this paper, we proposed the negatively pure association rules by negatively pure support, negatively pure confidence, and negatively pure lift to overcome the problems faced by negative association rule technique. In checking the usefulness of this technique through numerical examples, we could find the direction of association by the sign of the negatively pure association rule measure.