• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.341-348
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• 2000

의사결정 나무라고 불리우기도 하는 나무모형은 결과 해석의 용이성으로 데이터마이닝의 분류예측 모형으로써 큰 각광을 받고 있다. 현재 나무모형으로 가장 많이 사용되는 Breiman et. al의 CART나 Quinlan의 C4.5 모두 생성된 노드들의 자료 구성이 목표변수를 기준으로 수준 구성비 측면에서 순수해지도록 진행된다. 그러나 CRM에 있어 가장 흔한 주제인 해지예측을 위한 모델링을 실시하는 경우 관심의 대상인 해지자가 전체 자료에 극히 일부를 차지하여, 기존의 분할 방법에서와 같이 모든 노드의 순수성을 고려하기란 불가능하다. Buja와 Lee는 이와 같이 소수의 관심에 대상이 되는 부류를 찾아내기 위한 나무모형 생성방법을 소개하였다 즉, 해지자 관리가 중요한 경우 해지자와 비해지자 구분을 진행하는 기존의 방법과는 달리 전체 자료 중 해지자를 집중적으로 찾아가는 탐색적 분할 기준인 단측 순수성(one-sided purity)을 제안하였다. 본 연구에서는 단측 순수성에 의한 나무모델링을 모 PC통신 회사의 해지자 자료에 적용하며 기존의 방법과 비교하였고 몇 가지 시뮬레이션 자료를 통해 단측 순수성의 문제점과 앞으로 해결하여야 할 과제에 대하여 살펴보았다.

• 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 Analysis Society
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• v.20 no.6
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• pp.2865-2872
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• 2018

In the field of data mining technique, there are various methods such as association rules, cluster analysis, decision tree, neural network. Among them, association rules are defined by using various association evaluation criteria such as support, confidence, and lift. Agrawal et al. (1993) first proposed this association rule, and since then research has been conducted by many scholars. Recently, studies related to crossover entropy have been published (Park, 2016b). In this paper, we proposed a purely symmetric J measure considering directionality and purity in the previously published J measure, and examined its usefulness by using examples. As a result, it is found that the pure symmetric J measure changes more clearly than the conventional J measure, the symmetric J measure, and the pure crossover entropy measure as the frequency of coincidence increases. The variation of the pure symmetric J measure was also larger depending on the magnitude of the inconsistency, and the presence or absence of the association was more clearly understood.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.17-25
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• 2001

Tree model is the most popular classification algorithm in data mining due to easy interpretation of the result. In CART(Breiman et al., 1984) and C4.5(Quinlan, 1993) which are representative of tree algorithms, the split fur classification proceeds to attain the homogeneous terminal nodes with respect to the composition of levels in target variable. But, fur instance, in the chum prediction modeling fur CRM(Customer Relationship management), the rate of churn is generally very low although we are interested in mining the churners. Thus it is difficult to get accurate prediction modes using tree model based on the traditional split rule, such as mini or deviance. Buja and Lee(1999) introduced a new split rule, one-sided purity for classifying minor interesting group. In this paper, we compared one-sided purity with traditional split rule, deviance analyzing churning vs. non-churning data of ISP company. Also reviewing the result of tree model based on one-sided purity with some simulated data, we discussed problems and researchable topics.

• 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.

• Culinary science and hospitality research
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• v.21 no.6
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• pp.167-181
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• 2015

The purpose of this study is to examine the effects of genuineness, usefulness, overstatement, and assentation of the smartphone majib app on trust, perceived risk, and use intention, and thereby suggest useful information for the mobile application. A survey was conducted from May 11, 2015 to June 30, 2015 targeting smartphone majib app users through convenience sampling. A total of 300 questionnaires were distributed, of which 275 were used for analysis after excluding 25 response for negligent or inappropriate responses. The results found that, first, of the review characteristics, genuineness and usefulness, assentation had positive (+) effects on trust, while overstatement had a negative (-) effect on trust. Second, of the review characteristics, only genuineness and usefulness had significant effects on perceived risk. Third, trust had a significant effect on use intention rather than on perceived risk. Fourth, trust and perceived risk had mediating effects on the relationship between the assentation of the majib smartphone app review characteristics and use intention.

• 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.

• 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 Society for Precision Engineering
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• v.15 no.12
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• pp.220-225
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• 1998

압축성 초탄성 평판의 순수굽힘에 대한 비선형 변형해석의 수학적 정해가 본 논문에 구해져 있다. 이차원 평면 변형도 상태가 해석을 위하여 가정되었으며, 비선형 순수굽힘 변형해석결과는 고전적인 선형 순수굽힘 변형해석결과와 비교되었다. 고전적인 선형굽힘 결과와는 다르게 비선형 순수굽힘 상태에서는 반경방향응력은 영이 아니며 또한 각방향응력도 선형 상태가 아닌 것으로 규명되었다.

• Journal of Life Science
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• v.18 no.7
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• pp.907-911
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• 2008

Radish (Raphanus sativus L.) is one of very important crop plants in the world. It is very important to determine hybrid seed quality in the production of hybrid Brassica vegetable seeds to avoid unacceptable contamination with self-inbred (sib) seeds. The allozyme for evaluating seed purity in a commercial $F_1-hybrid$ radish cultivar is demonstrated. Three hundred sixty seeds from the male and female harvest were subsequently screened for seed purity using 27 isozyme loci. Especially, F1 hybrids of radish, Per-1 ($aa{\times}bb$), Lap-1 ($aa{\times}bb$), Est-1 ($aa{\times}bb$) were presented clear hybrid bands. Est-1 locus revealed that 15 (8.3%) seeds from the female harvest and 26 (14.4%) seeds from the male harvest were sibs. It maintains higher than average level of genetic diversity compared with their correspondent parents. Shannon's index of phenotypic diversity (I) of hybrids was the highest of all accessions (R. sativus L. cv. Daepeng, R. sativus L. cv. Backza, and their hybrids). The allozyme may lead to a better insight into the hybrid seed purity.