• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.235-243
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• 2011

The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between each set of items based on the association thresholds such as support, confidence, lift, etc. There are many interestingness measures as the criteria for evaluating association rules. 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 measure was developed to compensate for this drawback, but it is useless in the case that the value of positive confidence is the same as that of negative confidence. This paper propose a attributably pure confidence to evaluate association rules and then describe some properties for a proposed measure. The comparative studies with confidence, net confidence, and attributably pure confidence are shown by numerical example. The results show that the attributably pure confidence is better than confidence or net confidence.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.601-609
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• 2014

The most widely used data mining technique for big data analysis is to generate meaningful association rules. This method has been used to find the relationship between set of items based on the association criteria such as support, confidence, lift, etc. Among them, confidence is the most frequently used, but it has the drawback that we can not know the direction of association by it. The attributably pure confidence was developed to compensate for this drawback, but the value was changed by the position of two item sets. In this paper, we propose four symmetrically and attributably pure confidence measures to compensate the shortcomings of confidence and the attributably pure confidence. And then we prove three conditions of interestingness measure by Piatetsky-Shapiro, and comparative studies with confidence, attributably pure confidence, and four symmetrically and attributably pure confidence measures are shown by numerical examples. The results show that the symmetrically and attributably pure confidence measures are better than confidence and the attributably pure confidence. Also the measure NSAPis found to be the best among these four symmetrically and attributably pure confidence measures.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1127-1135
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• 2012

Association rule mining is the method to quantify the relationship between each set of items in a large database. One of the well-studied problems in data mining is exploration for association rules. There are three primary quality measures for association rule, support and confidence and lift. We generate some association rules using confidence. Confidence is the most important measure of these measures, but it is an asymmetric measure and has only positive value. Thus we can face with difficult problems in generation of association rules. In this paper we apply the similarity measures by probabilistic interestingness measure to find a solution to this problem. The comparative studies with support, two confidences, lift, and some similarity measures by probabilistic interestingness measure are shown by numerical example. As the result, we knew that the similarity measures by probabilistic interestingness measure could be seen the degree of association same as confidence. And we could confirm the direction of association because they had the sign of their values.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1113-1121
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• 2011

Recently, a variety of data mining techniques has been applied in various fields like healthcare, insurance, and internet shopping mall. Association rule mining is a popular and well researched method for discovering interesting relations among large set of data items. 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 three primary quality measures for association rules; support and confidence and lift. In this paper we consider some similarity measures with negative co-occurrence frequencies which is widely used in cluster analysis or multi-dimensional analysis as association thresholds. The comparative studies with support, confidence and some similarity measures are shown by numerical example.

• 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.27 no.2
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• pp.353-362
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• 2016

By Wikipedia, data mining is the process of discovering patterns in a big data set involving methods at the intersection of association rule, decision tree, clustering, artificial intelligence, machine learning. and database systems. Association rule is a method for discovering interesting relations between items in large transactions by interestingness measures. Association rule interestingness measures play a major role within a knowledge discovery process in databases, and have been developed by many researchers. Among them, the Hellinger measure is a good association threshold considering the information content and the generality of a rule. But it has the drawback that it can not determine the direction of the association. In this paper we proposed a signed Hellinger measure to be able to interpret operationally, and we checked three conditions of association threshold. Furthermore, we investigated some aspects through a few examples. The results showed that the signed Hellinger measure was better than the Hellinger measure because the signed one was able to estimate the right direction of association.

• Proceedings of the Korean Society for Information Management Conference
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• 1998.08a
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• pp.109-114
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• 1998

학술지의 평가는 일반적으로 이용빈도와 인용빈도를 이용하여 이루어졌으나 시간과 비용, 인력문제에 따라 그 효용성에 많은 의문이 제기되고 있다. 본 연구는 이점에 착안하여 학술지를 평가하는 기준으로 학술지가 갖고 있는 외형적 정보를 활용하는 방안을 고찰하였다. 또한, 인용분석도 병행하여 외형적 평가기준과의 연관성도 조사하였다. 새로운 평가방법의 타당성을 측정하기 위해 학문 전반적인 연구에 앞서 일문학분야를 대상으로 기본적인 실험도 수행되었다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.540-542
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• 1999

본 논문에서는 기업내의 지식활동 주체들이 지식경영을 실천하는데 필수적인, 지식에 대한 가치평가 기준을 제시한다. 이 평가기준은 기업의 경영전략적 모형의 구조에서 도출하였다. 지식경영의 궁극적 목적을 기업의 경영전략의 실현으로 파악하여, 지식의 가치를 기업의 사명, 비전, 경영목표 및 경영전략 등이 형성해내는 기업의 경영전략적 모형안에서 찾았다. 기업의 경영전략적 모형의 구조안에서, 업무처리 지식에 대한 가치측정은, 이 구조가 제한하는 단일지식에 대한 완성도와 복잡도로 평가하였다. 그리고 기업의 경영방향에 관련된 지식의 가치는, 그 구조요소들 사이의 중요도와 연관성으로 평가하였다.

• Journal of Oral Medicine and Pain
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• v.34 no.3
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• pp.325-331
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• 2009

The objective of this study was to describe the prevalence of the headache attributed to Temporomandibular disorder(TMD) symptoms and to investigate the relationships of headache and TMD. 66 patients seeking care for signs and symptoms of Temporomandibular disorders(TMD) and Orofacial pain in the department of oral medicine, Dental Hospital, Chosun University, from January, 2008 to June, 2008, were recruited. The obtained results were as follows : 1. A muscle and TMJ origin combined was the most common in study populations(54.55%), grouped as TMD classification. 2. Tension type headache was the most common in study population(89.39%), grouped as headache classification. 3. 36 patients out of 66(54.55%) had headaches which related to TMD. 4. Out of 36 patients who had suffered the headache which were attributed to TMD, 19 patients(52.78%) described that their headache related to TMD was different from their own primary headaches. In conclusion, headache attributed to TMD is relatively common in the patients who had headaches and TMD symptoms together. And the new headache patterns may related to headache and TMD chronification. Larger-scale studies and more specified and controlled comparison study is needed to confirm the relationship between the headache and TMD.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.293-293
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• 2022

가뭄은 시간적 지속성이 타 재난에 비해 길고 공간적 피해의 범위와 편차가 크다는 점에서 지역적 특성과 가뭄대응역량에 맞는 적절한 대응과 대책 마련이 필수적이다. 본 연구에서는 선행연구에서 제시된 바 있는 지역적 가뭄복원력평가 방법론을 기준으로, 가뭄복원력 평가 인자의 민감도 분석을 실시하였다. 가뭄복원력평가 인자는 4Rs (Robustness, Redundancy, Resourcefulness, Rapidity)을 기준으로 총 18개의 지표로 구성되어 있으며, 18개 지표를 산출하는 과정에서 활용되는 세부자료는 정량자료 19개와 담당자 설문조사를 통해 산정되는 정성자료 8개를 포함하여 총 28개가 존재한다. 본 연구에서는 국내 지자체의 복원력 평가 결과 기준, 등급별 1-2개 지자체를 민감도 분석의 대상으로 설정하고, 19개 정량자료 각각의 비율적 변화에 따른 복원력 결과의 변동성을 정량화하여 도출하였다. 또한 19개 정량자료 중, 연관성 및 계층적 관계성이 존재하는 주요 자료 그룹을 구분하고, 대상 자료의 동시적, 연쇄적 변화에 따른 복원력 평가 결과의 영향도를 정성, 정량적으로 평가하였다. 분석 결과 가뭄 복원력에 기여하는 인자의 변화에 따라 가뭄 복원력의 증감 정도가 지자체 별도 상이하게 나타남을 확인할 수 있었다. 도출된 민감도 분석 결과는 지자체의 현재 가뭄대응역량지표를 기준으로, 가뭄 복원력을 증가시키기 위한 효율적 대책 및 계획 수립을 위한 의사결정에 활용될 수 있을 것으로 기대된다.