• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.997-1008
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• 2006

For extracting and processing information explicitly from given data, Formal Concept Analysis(FCA) is provided a method which is widely used for data analysis and clustering. The data can be structured into concepts, which are formal abstractions human thought allowing meaningful comprehensible interpretation. However, most FCA tools mainly focus on analyzing one-valued contexts that represent objects, attributes and binary relations between them. There we few FCA tools available that provide scaling and analyzing many-valued contexts representing objects, attributes and relations with attributes' values. In this paper, we propose not only a scaling algorithm for interpreting and simplifying the multivalued input data, but also an algorithm to generate concepts and build concept hierarchy from given raw data as well. Based on these algorithms, we develop an automate tool, FCA Wizard, for concept analysis and concept hierarchy. We also present FCA Wizard based applications in medical domain.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.33-39
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• 2024

In a previous study we proceeded to the remodularization architecture based on classes and packages using the Formal Concept Analysis (FCA)[13] [14] [30]. we then got two possible remodularized architectures and we explored the issue of redistributing classes of a package to other packages, we used an approach based on Oriented Graph to determine the packages that receive the redistributed classes and we evaluated the quality of a remodularized software architecture by metrics [31] [28] [29]. In this paper, we will address the issue of the efficiency of the Oriented Graph in the remodularization of software architectures compared to the Formal Concept Analysis FCA method. The formal method of FCA concept is not popularized among scientists as opposed to the use of the labeled directed graph. It is for this reason that our directed graph approach is more effective in its simplicity and popularity.

• Journal of Information Processing Systems
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• v.11 no.2
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• pp.184-204
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• 2015

Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.

• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.123-129
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• 2016

This study had a purpose to find the correlation between academic achievement and entrance examination score using FCA and framing a concept of university academic achievement and entrance examination score and then, building concept lattice. The subject for this analysis was admission materials of freshmen in computer engineering department at A university located in Chungcheong area in 2010. Also 56 students in total were subjects for the score analysis of their first semester from 2010 to 2014. The major findings of this study were as follows. We organized and extracted many-valued context, concept lattice, associated rules using FCA. And we analyzed the correlation between University academic achievement and entrance examination score using FCA. The conclusions were as follows. We could get meaningful analyzed results using FCA. University academic achievement had lower relativeness with entrance examination score and female students had higher university academic achievement than male students. The improvement points are summarized below. It is necessary to improve the ratio and application method of the university admission factors and to increase the regular admission for a university. And programs for completing College-preparatory programming for male students in low university academic achievement are necessary.

• Industrial Engineering and Management Systems
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• v.9 no.1
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• pp.1-9
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• 2010

Increasing product variety based on product family and product platform provides a company with a competitive advantage over its competitors. As products become more complex, short-life cycled and customized, the design efforts require more knowledge-intensive, collaborative and coordinating efforts for information sharing. By sharing knowledge, information, component and process across different families of products, the product realization process will be more efficient, cost-effective and quick-responsive. Formal Concept Analysis (FCA) is used for analyzing data and forming semantic structures that are formal abstractions of concepts of human thoughts. A Web Ontology Language (OWL) is designed for applications that need to process the content of information instead of simply presenting information to humans. OWL also captures the evolution of different components of the product family. The purpose of this paper is to develop product variety modeling to increase the usefulness of common platform. In constructing and analyzing product ontology, FCA is adopted for conceptual knowledge processing. For the selected product family, product variety Ontology is constructed and implemented using prot$\'{e}$g$\'{e}$-2000.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.19-30
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• 2012

Based on the internet-based infrastructures such as various information devices, social network systems and cloud computing environments, distributed and sharable data are growing explosively. Recently, as a data analysis and mining technique for extracting, analyzing and classifying the inherent and useful knowledge and information, Formal Concept Analysis on binary or many-valued data has been successfully applied in many diverse fields. However, in formal concept analysis, there has been little research conducted on analyzing interval data whose attributes have some interval values. In this paper, we propose a new approach for classification of interval data based on the formal concept analysis. We present the development of a supporting tool(iFCA) that provides the proposed approach for the binarization of interval data table, concept extraction and construction of concept hierarchies. Finally, with some experiments over real-world data sets, we demonstrate that our approach provides some useful and effective ways for analyzing and mining interval data.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.479-493
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• 2010

Along with the transformation of the knowledge-based environment, e-learning has become a main teaching and learning method, prompting various research efforts to be conducted in this field. One major research area in e-learning involves adaptive learning systems that provide personalized learning content according to each learner's characteristics by taking into consideration a variety of learning circumstances. Active research on ontology-based adaptive learning systems has recently been conducted to provide more efficient and adaptive learning content. In this paper, we design and propose an adaptive learning system based on the concept lattice of Formal Concept Analysis (FCA) with the same objectives as those of ontology approaches. However, we are in pursuit of a system that is suitable for learning of specific domains and one that allows users to more freely and easily build their own adaptive learning systems. The proposed system automatically classifies the learning objects and concepts of an evolved domain in the structure of a concept lattice based on the relationships between the objects and concepts. In addition, the system adaptively constructs and presents the learning structure of the concept lattice according to each student's level of knowledge, learning style, learning preference and the learning state of each concept.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.251-254
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• 2005

계층적 개념구조는 대상 도메인으로부터 지식을 표현하는데 있어서 간결하면서도 효과적으로 개념간의 구조를 설명하기 위해 사용되고, 데이터의 구조화와 요약을 제공하며 필요한 정보의 수월한 접근을 제공하기 위해 널리 적용되고 있다. 그러나, 대상도메인으로부터 계층적 개념구조를 구축하기 위해서는 많은 시간과 노력이 요구된다. 따라서 계층적 개념구조를 효율적으로 구축하기 위한 체계가 필요하다. 본 논문에서는 계층적 개념구조를 체계적으로 구축하기 위한 형식개념분석기법(FCA, Formal Concept Analysis)을 토대로, Many-valued context와 Scaling을 자동화하기 위한 도구로써 본 연구에서 개발된 FCA Wizard를 소개한다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.91-96
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• 2008

국토 모니터링을 위해서는 다양한 출처와 종류의 자료를 통합하여 제공하여야 한다. 특히 국토 모니터링의 범위에는 원격탐사와 같은 공중 모니터링과 지상 모니터링 자료가 모두 포함되기 때문에 매우 다양한 종류와 속성을 가진 지리정보 자료들이 통합되어야 한다. 본 연구에서는 다양한 출처와 종류의 지리정보자료를 효과적으로 통합하는 방법으로 형식개념분석(Formal Concept Analysis, FCA)을 살펴보고 사례분석을 통해 이 기법의 적용 가능성을 파악하고자 한다. 연구결과 형식개념분석을 통하여, 다양한 종류의 자료가 가지고 있는 중복을 제거하고 이를 체계적으로 정리하여 통합할 수 있다. 본 연구에서는 형식개념 분석의 개념을 파악하고 현재 활용되고 있는 지리정보자료들에 적용하여 평가함으로써 국토 모니터링 자료의 통합기법으로 적용될 수 있는 가능성을 연구하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.275-278
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• 2004

객체지향 프로그램에 있어 클래스계층구조는 프로그램의 뼈대가 된다. 따라서 이러한 클래스계층구조를 얼마나 잘 만드느냐에 따라 프로그램의 품질이 좌우된다. 그러나 좋은 품질의 클래스계층구조를 구축하는 작업은 객체지향 초보자에게는 쉬운 일이 아니다. 본 논문에서는 FCA(Formal Concept Analysis)기법을 이용하여 클래스계층구조 설계 도구를 BlueJ 의 확장기능으로 구현하였다. 본 연구결과는 객체지향 프로그래밍 초보자들이 클래스계층구조를 보다 수월하게 설계함으로써 좀 더 좋은 프로그램을 작성 할 수 있는 지원도구로서 제공될 수 있다.