• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.523-532
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• 2013

Generally, data mining is the process of analyzing big data from different perspectives and summarizing it into useful information. The most widely used data mining technique is to generate association rules, and it finds the relevance between two items in a huge database. This technique has been used to find the relationship between each set of items based on the interestingness measures such as support, confidence, lift, etc. Among many interestingness measures, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The attributably pure confidence and compared confidence are able to determine the direction of the association, but their ranges are not [-1, +1]. So we can not interpret the degree of association operationally by their values. This paper propose a compared and attributably pure confidence to compensate for this drawback, and then describe some properties for a proposed measure. The comparative studies with confidence, compared confidence, attributably pure confidence, and a proposed measure are shown by numerical example. The results show that the a compared and attributably pure confidence is better than any other confidences.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.611-618
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• 2015

By Wikipedia, big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Data mining is the computational process of discovering patterns in huge data sets involving methods at the intersection of association rule, decision tree, clustering, artificial intelligence, machine learning. Association rule is a well researched method for discovering interesting relationships between itemsets in huge databases and has been applied in various fields. There are positive, negative, and inverse association rules according to the direction of association. If you want to set the evaluation criteria of association rule, it may be desirable to consider three types of association rules at the same time. To this end, we proposed a balanced comparative confidence considering sensitivity, specificity, false positive, and false negative, checked the conditions for association threshold by Piatetsky-Shapiro, and compared it with comparative confidence and inversely comparative confidence through a few experiments.

• Journal of the Korean Statistical Society
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• v.5 no.2
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• pp.109-118
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• 1976

현행 일반적으로 쓰여지고 있는 물가지수 산식은 기준시점의 거래량(또는 거래금액)을 상품별 가중치(weight)로 삼는 가중총합방식(weighted aggregate formula, 또는 가중산술평균산식)으로서의 Laspeyres식이라 함은 주지하는 바와 같다. 그것이 상품별로 유통면의 중요성을 분명히 감안하여 있고, 비교시점의 가격변동만이 계산에 반영된다는 점에 있어서 물가지수로서의 실용성이 널리 인정되어 있는 산식이다. 그러나 Lasperyres식의 난점을 또한 많은 것이니 그 가운데 특히 가중치의 고정성과 관련하여 기준시점의 이동에 따른 전후 물가지수의 비연결성은 결정적 결함이라 할 수 있다. 여기에 이 식의 지수적 허구성이 흔히 논의되고, 이른바 Paasche check라 하여 수시로 조사한 거래량(또는 거래금액)에 의하여 물가지수의 가중치로 삼아서 전자를 검정하는 방법도 쓰여지는 형편이다. 필지는 일찌기(1973년) Laspeyres식의 상품별 가중치에 관한 객관적 평가법의 하나로서 산업(따라서 상품)의 연관분석적 수단에 의한 약간의 시안을 발표한 바 없지 않았다. 그것은 요약컨대 산업연관분석에 쓰이는 투입계수표를 중심삼아 한 상품가격이 다른 상품가격에 미치는 파급효과, 따라서 물가에 미치는 파급력을 계산하고, 나아가서 각 상품의 수요 및 공급함수를 도입하여 그들 계수를 추정함으로써 가중치의 객관화를 꾀해 본 것이 전고의 골자이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04b
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• pp.1085-1088
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• 2001

현대 사회는 정보통신 기술의 발달로 정보 시스템의 사용이 급격히 증가 되고 있다. 정보화의 가속화에 따라 다양한 역기능들 또한 도출되고 있다. 여러 가지 역 기능들로부터 정보를 보호하고 안정한 정보 유통을 위한 양질의 정보보호 시스템을 위해 정보보호 제품에 대한 평가가 요구 되고 있다. 이런 이유로 전세계적으로 많은 평가 기준들이 만들어지고 있으며, 국내에서도 침입차단 시스템에 대한 평가기준을 만들고 이를 사용해 평가를 하고 있다. 본 논문에서는 국내 침입차단 시스템 평가 기준과 소프트웨어 프로세스 심사방법 중 하나인 SPICE 사이의 연관성을 찾아보고자 한다.

• 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.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.557-564
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• 2003

This paper analyzes the side channel attacks for smartcard devices, and proposes the smartcard suity evaluation criteria for side-channel attacks. To setup the smartcard security evaluation criteria for side-channel attacks, we analyze similar security evaluation criteria for cryptographic algorithms, cryptographic modules, and smartcard protection profiles based on the common criterion. Futhermore, we propose the smartcard security evaluation criteria for side-channel attacks. It can be useful to evaluate a cryptosystem related with information security technology and in addition, it can be applied to building smartcard protection profile.

• The Journal of Society for e-Business Studies
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• v.16 no.2
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• pp.39-71
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• 2011

Lately, developing systems fully interoperable with others is considered an essential element for successful projects, as not only do e-commerce becomes ubiquitous but also distributed systems' paradigm spreads. However, since definitions of interoperability vary by viewpoints, it is still difficult to have the same understanding and evaluation criteria on interoperability. For instance, various interoperability parties in military use different definitions of interoperability, and its T&E is not conducted according to the definition, but only to levels of information exchange. In this paper, we proposed a new definition of interoperability as followsm First of all, we collected existing and various interoperability definitions, extracting key components in each of them. Second, we statistically analyzed those components and applied the association rules discovery in data mining. We compared existing interoperability definitions to ours. From this research, we found associations among the components from various definitions applying market-basketanalysis, redefining interoperability. Key findings of this research can contribute to a unified viewpoint on the definition, level, and evaluation items of interoperability.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1147-1154
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• 2010

One of the important goals in data mining is to discover and decide the relationships between different variables. Association rules are required for this technique and it find meaningful rules by quantifying the relationship between two items based on association measures such as support, confidence, and lift. In this paper, we presented the evaluation criteria of weighted association rule considering item RFM scores as importance of items. Original RFM technique has been used most widely applied method using customer information to find the most profitable customers. And then we compared general association rule technique with weighted association rule technique through the simulation data.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.49-56
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• 2017

Among the techniques for analyzing big data, the association rule mining is a technique for searching for relationship between some items using various relevance evaluation criteria. This associative rule scheme is based on the direction of rule creation, and there are positive, negative, and inverse association rules. The purpose of this paper is to investigate the applicability of various types of relatively causal strength measures to the types of association rules from the point of view of interestingness measure. We also clarify the relationship between various types of confidence measures. As a result, if the rate of occurrence of the posterior item is more than 0.5, the first measure ($RCS_{IJ1}$) proposed by Good (1961) is more preferable to the first measure ($RCS_{LR1}$) proposed by Lewis (1986) because the variation of the value is larger than that of $RCS_{LR1}$, and if the ratio is less than 0.5, $RCS_{LR1}$ is more preferable to $RCS_{IJ1}$.

• 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.