• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1189-1197
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• 2013

Data mining is the process of analyzing a huge database from different perspectives and summarizing it into useful information. One of the well-studied problems in data mining is association rule generation. Association rule mining finds the relationship among several items in massive volume database using the interestingness measures such as support, confidence, lift, etc. Typical applications for this technique include retail market basket analysis, item recommendation systems, cross-selling, customer relationship management, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. This paper propose causal association thresholds to compensate for this problem, and then check the three conditions of interestingness measures. The comparative studies with basic and causal association thresholds are shown by numerical example. The results show that causal association thresholds are better than basic association thresholds.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5379-5388
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• 2012

In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.235-237
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• 2002

연관규칙(association rule)이란 데이터 베이스에 존재하는 속성들 사이에 유사성 또는 패턴을 기술하는 것으로, 사용자에게 데이터에 관한 유용한 조보를 줄 수 있다. 그러나, 지금가지의 연관규칙은 이진 (boolean) 데이터 베이스에 존재하는 연관규칙의 발견에 대해서 주로 연구되어 왔으며, 정량적(수치적, quantitative) 속성을 갖는 데이터에 대한 연관규칙의 연구는 미비하였다. 그 이유는 정량적 속성을 갖는 데이터를 기호적(nominal) 속성값으로 바꾼 후 연관규칙 보다 성능이 우수함을 보이고 있다. 또한 본 논문에서는 퍼지 연관규칙에서 소속함수(항목, 아이템, 속성값)의 모양과 개수를 데이터 분포에 대한 통계적 특성을 나타내는 히스토그램을 이용하여 소속함수를 자동 생성하는 효율적인 연관규칙 추출방법을 제안한다

• Journal of Korea Multimedia Society
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• v.10 no.5
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• pp.652-667
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• 2007

With the wide spread of XML data in various fields, the interest in developing convenient user interfaces for inputting XML data is increasing. Previous works on the automated generation of user interfaces have given insufficient supports for various user environments and inconvenient editing tools for complex documents. In this paper, we presents an approach to generate XForms based user interfaces from XML schemas, for inputting XML data. The proposed method consists of three steps: parsing XML schemas, generating XForms codes, and embedding it into host language documents. Specifically, to generate XFonns codes, the rules for generating XForms codes, which support complex types and map simple types to appropriate input controls, are proposed. Experimental results with various kind of schemas show that the proposed method can generate XForms interfaces successfully.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.3
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• pp.81-107
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• 2015

This study describes the Bottom-up method for implementation of an ontology system from the RDB. The STNet, a structured terminology glossary based on RDB, was served as a test bed for converting to RDF ontology, for generating the inference rules, and for evaluating the results of the semantic search. We have used protege editor of the ontology developing tool to design ontologies with test data. We also tested the designed ontology with the Inference Engine (Pellet) of protege editor. The generated reference rules were tested by TBox and SPARQL queries through STNet ontology. The results of test show that the generated reference rules were verified as true and STNet ontology were also evaluated to be useful for searching the complex combination of semantic relation.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.27-32
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• 2003

The rule induction system generates a set of inductive rules, and the task of selecting an optimal rule subset is one of the important problem in the area of rule induction. This paper proposes a new learning method which combines rule induction system with the paradigm of genetic algorithm. This paper shows that genetic algorithm can be effectively applied to optimal rule selection problem. The proposed system was evaluated using a set of different machine learning data sets and, showed better performance in all cases than other traditional methods.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.85-87
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• 2005

프로그래밍 기술과 인터넷 통신의 발달로 인하여 보안성이 검증되지 않은 다양한 프로그램들이 생성되고 쉽게 유포되어 보안 취약성으로 인해 야기되는 다양한 문제의 심각성이 더해가고 있다. 따라서 사용자가 보안상 안전하게 사용할 수 있는 소프트웨어 인증절차가 필수적으로 요구되고 있는데, 이를 해결하기 위해 소프트웨어 안전성 평가에 대한 연구가 진행 중이지만, 기존의 방법들은 특정 영역에 한정적이어서 일반적인 소프트웨어의 보안성 평가(security evaluation) 방법으로써 부적합하다. 뿐만 아니라 기존의 시스템들은 단순 패턴매칭에 기반을 두고 있어 오용탐지가 크고 정확성이 떨어진다는 문제점을 가지고 있다. 따라서 본 논문에서는 이러한 문제점들을 해결하기 위해 악성프로그램 코드의 구조와 흐름을 분석하여 규칙으로 정의하고 그 규칙에 따라 검사 대상 프로그램 코드에서 악성코드와 취약점 흐름을 탐지하는 규칙 기반의 소프트웨어 보안성 검증 시스템 프로토타입을 제안한다. 제안한 검증 시스템의 프로토타입은 악성코드와 소프트웨어 취약성을 동시에 탐지하여 보안성을 평가함으로써 범용적인 소프트웨어 평가에 활용 가능할 것이다.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.402-407
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• 2001

대용량의 데이터베이스로부터 연관규칙을 발견하고자 하는 연구가 활발하며, 수량 데이터의 항복에도 적용할 수 있도록 이들 방법을 확장하는 연구가 최근에 소개되고 있다. 본 논문에서는 수량 데이터 항목을 이진 항목으로 변환하기 위하여 빈발구간 항목집합을 생성할 때, 수량 데이터 항목의 정의 영역 내에서 특정 영역에 집중하여 발생하는 특성인 지역성을 이용하는 방법을 제안한다. 이 방법은 기존의 방법보다 많은 수의 세밀한 빈발구간 항목들을 생성할 수 있을 뿐만 아니라 세밀의 정도를 판단하여 활용할 수 있는 생성순서 정보도 포함하고 있어, 원 데이터가 가지고 있는 특성의 손실을 최소화한 수 있는 특징이 있다. 성능평가를 통하여 기존의 방법보다 우수함을 보였다.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.891-899
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• 2010

Association rule is the technique to represent the relationship between two or more items by numerical representing for the relevance of each item in vast amounts of databases, and is most being used in data mining. The basic thresholds for association rule are support, confidence, and lift. these are used to generate the association rules. We need standardization of lift because the range of lift value is different from that of support and confidence. And also we need standardization of support and confidence to compare objectively association level of antecedent variables for one descendant variable. In this paper we propose a method for standardization of association thresholds considering marginal probability for each item to grasp objectively and exactly association level, check the conditions for association criteria and then compare association thresholds with standardized association thresholds using some concrete examples.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.378-384
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• 2013

In this paper, we propose the fuzzy neural networks based on fuzzy c-means clustering algorithm. Typically, the generation of fuzzy rules have the problem that the number of fuzzy rules exponentially increases when the dimension increases. To solve this problem, the fuzzy rules of the proposed networks are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the learning of fuzzy neural networks is realized by adjusting connections of the neurons, and it follows a back-propagation algorithm. The proposed networks are evaluated through the application to nonlinear process.