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

• The Journal of the Korea Contents Association
• /
• v.18 no.1
• /
• pp.305-313
• /
• 2018

Public open data is a database or electronic file produced by a public agency or government. The government is opening public data through the open data portals and individual agency websites. However, it is a reality that there is a limit to search and utilize desired public data from the perspective of data users. In particular, it takes a great deal of effort and time to understand the characteristics of data sets and to combine different data sets. This study suggests the possibility of interlinking between data sets by analyzing the common relationship of item names held by public data. The data sets are collected from the open data portal, and item names included in the data sets are extracted. The extracted item names consist of formal context and formal concept through formal concept analysis. The format concept has a list of data sets and a set of item name as extent and intent, respectively, and analyzes the common items of intent end to determine the possibility of data connection. The results derived from the formal concept analysis can be effectively applied to the semantic connection of the public data, and can be applied to data standard and quality improvement for public data release.

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

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

• The KIPS Transactions:PartD
• /
• v.15D no.3
• /
• pp.429-440
• /
• 2008

Formal Concept Analysis (FCA) is a widely used methodology for data analysis, which extracts concepts and builds a concept hierarchy from given data. A concept consists of objects and attributes shared by those objects, and a concept hierarchy includes information on super-sub relations among the concepts. In this paper, we propose a method for extracting Implication and Association rules from a concept hierarchy given a query by a user. The method also describes a way for displaying the extracted rules. Based on this method, we implemented an automatic tool, QAG-Wizard. Because the QAG-Wizard not only elicits relation information for the given query, but also displays it in structured form intuitively, we expect that it can be used in the fields of data analysis, data mining and information retrieval for various purposes.

• Journal of Information Processing Systems
• /
• v.6 no.2
• /
• pp.197-208
• /
• 2010

Test scenarios can be derived based on some system models for requirements validation purposes. Model-based test suite reduction aims to provide a smaller set of test scenarios which can preserve the original test coverage with respect to some testing criteria. We are proposing to apply Formal Concept Analysis (FCA) in analyzing the association between a set of test scenarios and a set of transitions specified in a state machine model. By utilizing the properties of concept lattice, we are able to determine incrementally a minimal set of test scenarios with adequate test coverage.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.35 no.3
• /
• pp.110-117
• /
• 2012

A product family design has received much attention over the last several decades, since a product family-based development shortens lead-times and reduces cost, as well as increases efficiency and effectiveness of the product realization process. It is challenging work, however, to define the product family design in the heterogeneous product development environments, due to myriads of products related information described in different ways across products in any companies. In this paper, we provided a way of defining product family design framework using formal concept analysis and ontology language. Based on this, the specific product family can be derived by ontological reasoning, and the new product concept can be also expanded in the framework. The proposed framework is formalized using OWL (Web Ontology Language) and implemented in $Prot{\acute{e}}g{\acute{e}}$. Actual product family design algorithm is carried out using FaCT++ engine, a plug-in to $Prot{\acute{e}}g{\acute{e}}$, and the benefits of the proposed method are also demonstrated through a case study.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.6
• /
• pp.57-68
• /
• 2017

The Linked Open Data(LOD) cloud is quickly becoming one of the largest collections of interlinked datasets and the de facto standard for publishing, sharing and connecting pieces of data on the Web. Data publishers from diverse domains publish their data using Resource Description Framework(RDF) data model and provide SPARQL endpoints to enable querying their data, which enables creating a global, distributed and interconnected dataspace on the LOD cloud. Although it is possible to extract structured data as query results by using SPARQL, users have very poor in analysis and visualization of RDF data from SPARQL query results. Therefore, to tackle this issue, based on Formal Concept Analysis, we propose a novel approach for analyzing and visualizing useful information from the LOD cloud. The RDF data analysis and visualization technique proposed in this paper can be utilized in the field of semantic web data mining by extracting and analyzing the information and knowledge inherent in LOD and supporting classification and visualization.

• Journal of Information Processing Systems
• /
• v.19 no.2
• /
• pp.189-202
• /
• 2023

In the era marked by information inundation, social network analysis is the most important part of big data analysis, with clique detection being a key technology in social network mining. Also, detecting maximal balance clique in signed networks with positive and negative relationships is essential. In this paper, we present two algorithms. The first one is an algorithm, MCDA1, that detects the maximal balance clique using the improved three-way concept lattice algorithm and object-induced three-way concept lattice (OE-concept). The second one is an improved formal concept analysis algorithm, MCDA2, that improves the efficiency of memory. Additionally, we tested the execution time of our proposed method with four real-world datasets.

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