• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (2)
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• pp.187-189
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• 2004

최근 정보기술의 가속적인 발전과 인터넷의 급속한 보급으로 인하여 우리는 다양하고 방대한 양의 지역정보를 접하고 이용하고 있다. 그러나 지역개발사업을 추진하는데 있어서 계획수립이 차지하는 중요성이 매우 큼에도 불구하고 지역을 대표하는 객관적이고 유용한 정보를 찾아내어 지역개발계획수립에 활용하는 예는 거의 없었다. 이에 여러 곳에 산재되어있는 지역정보들을 통합하여 관리하고 이러한 대량의 지역 데이터들로부터 지역을 특징지을 수 있는 보다 현실적이고 유용한 정보를 추출하거나 생성하여 지역정보 분석에 활용하는 방법이 필요하게 되었다. 본 논문에서는 지역개발계획을 수립하는데 있어서 방대한 양의 데이터로부터 유용한 정보를 추출하고 발견하는 지식발견(KDD : Knowledge Discovery in Database)(1) 프로세서의 전체과정에 지역개발계획 수립 목적에 맞추어 지역개발이론에 기초한 지역정보 분석과정을 삽입함으로써 보다 합리적이고 현실적인 지역개발계획이 수립되도록 지원할 수 있는 프로세서를 설계한다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1999년도 가을 학술발표논문집 Vol.26 No.2 (2)
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• pp.101-103
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• 1999

Decentralized 정보는 여러 도메인에 대한 heterogeneous한 독립적인 정보가 자율적으로 존재하며 이들 정보간의 관계성의 고려한 전체에 대한 global view가 존재하지 않기 때문에 inter-domain에 대한 마이닝을 수행하는데 어려움이 있다. 본 연구에서는 intra-domain knowledge discovery, intra 및 inter-domain problem solving method라는 접근방법으로, decentralized 데이터 환경에서 문제 해결에 필요한 정보 추출을 위한 데이터 tailoring과 분산 데이터에 대한 목표-지향 데이터마이닝(goal-oriented data-mining)을 통해 문제 해결을 위해 필요한 지식을 생성하고 이들 간의 관련 정보를 탐색하여 문제를 해결하는 프레임워크를 제안한다. 특히, 생성된 지식간의 협동 문제 처리를 멀티 에이전트 패러다임을 이용하기로 한다. 제안 프레임워크는 산재되어 있는 데이터들로부터 문제 해결에 유용한 지식 차원의 정보를 추출해내고 생성된 지식을 바탕으로 각 도메인 정보에 대한 개별적인 사용뿐 만 아니라 서로 cooperation을 통한 문제 해결을 지원함으로써, 개방된 분산 환경하에 decentralized 되어 있는 여러 도메인 정보를 보다 효율적으로 활용할 수 있는 새로운 형태의 문제 해결 방법이라고 할 수 있다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.2029-2032
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• 2004

The quantity of data were rapidly increased recently and caused the data overwhelming. This led to be difficult in searching the required data. The method of eliminating redundant data was needed. One of the efficient methods was Knowledge Discovery in Database (KDD). Generally data can be separate into 2 cases, continuous data and discrete data. This paper describes algorithm that transforms continuous attributes into discrete ones. We present an Improved Class Attribute Interdependence Maximization (ICAIM), which designed to work with supervised data, for discretized process. The algorithm does not require user to predefine the number of intervals. ICAIM improved CAIM by using significant test to determine which interval should be merged to one interval. Our goal is to generate a minimal number of discrete intervals and improve accuracy for classified class. We used iris plant dataset (IRIS) to test this algorithm compare with CAIM algorithm.

• 한국수학사학회지
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• 제18권2호
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• pp.95-106
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• 2005

본 논문에서는 수학을 기반으로 한 데이터베이스의 지식탐사 절차를 통하여 데이터의 선택, 정제, 통합, 변환, 축소, 데이터 마이닝 기법의 선택과 적용 및 모형의 평가에 관한 개념과 방법론을 소개하고 수학의 한 분야로서 통계학의 역할과 적용방법에 관하여 연구하고자 한다. 또한 오늘날 관심이 대상이 되고 있는 데이터 마이닝의 역사와 수학적 배경, 통계 및 정보 기술을 이용한 데이터 마이닝의 주요 모델링 기법, 실용적 응용 분야 및 적용 사례 그리고 데이터 마이닝과 통계의 차이점에 관하여 조사하고 논하고자 한다.

• 공학교육연구
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• 제15권4호
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• pp.41-47
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• 2012

Data mining and knowledge discovery techniques have shown to be effective in finding hidden underlying rules inside large database in an automated fashion. On the other hand, analyzing, assessing, and applying students' survey data are very important in science and engineering education because of various reasons such as quality improvement, engineering design process, innovative education, etc. Among those surveys, analyzing the students' views on science-technology-society can be helpful to engineering education. Because, although most researches on the philosophy of science have shown that science is one of the most difficult concepts to define precisely, it is still important to have an eye on science, pseudo-science, and scientific misconducts. In this paper, we report the experimental results of applying decision tree induction algorithms for analyzing the questionnaire results of high school students' views on science-technology-society (HS-VOSTS). Empirical results on various settings of decision tree induction on HS-VOSTS results from one South Korean university students indicate that decision tree induction algorithms can be successfully and effectively applied to automated knowledge discovery from students' survey data.

• ETRI Journal
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• 제32권5호
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• pp.676-686
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• 2010

Mining sequential patterns is an important research issue in data mining and knowledge discovery with broad applications. However, the existing sequential pattern mining approaches consider only binary frequency values of items in sequences and equal importance/significance values of distinct items. Therefore, they are not applicable to actually represent many real-world scenarios. In this paper, we propose a novel framework for mining high-utility sequential patterns for more real-life applicable information extraction from sequence databases with non-binary frequency values of items in sequences and different importance/significance values for distinct items. Moreover, for mining high-utility sequential patterns, we propose two new algorithms: UtilityLevel is a high-utility sequential pattern mining with a level-wise candidate generation approach, and UtilitySpan is a high-utility sequential pattern mining with a pattern growth approach. Extensive performance analyses show that our algorithms are very efficient and scalable for mining high-utility sequential patterns.

• International Journal of Contents
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• 제12권4호
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• pp.8-16
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• 2016

This research proposes the composition logic of an Active Learning Environment (ALE), to enable discovery by learning through experience, whilst increasing knowledge about modern architectural heritage. Linking information to the historical heritage using Information and Communication Technology (ICT) helps to overcome the limits of previous learning methods, by providing rich learning resources on site. Existing field trips of cultural heritages are created to impart limited experience content from web resources, or receive content at a specific place through humanities Geographic Information System (GIS). Therefore, on the basis of the blended space theory, an augmented space experience method for overcoming these shortages was composed. An ALE space framework is proposed to enable discovery through learning in an expanded space. The operation of ALE space is needed to create full coordination, such as a Content Management System (CMS). It involves a relation network to provide knowledge to the rule engine of the CMS. The application is represented with the Deoksugung Palace Seokjojeon hall example, by describing a user experience scenario.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.1-6
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• 2003

Associations, as specific forms of knowledge, reflect relationships among items in databases, and have been widely studied in the fields of knowledge discovery and data mining. Recent years have witnessed many efforts on discovering fuzzy associations, aimed at coping with fuzziness in knowledge representation and decision support processes. This paper focuses on associations of three kinds, namely, association rules, functional dependencies and pattern associations, and overviews major fuzzy logic extensions accordingly.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1998년도 국제 컨퍼런스: 국가경쟁력 향상을 위한 디지틀도서관 구축방안
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• pp.83-98
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• 1998

Who needs Media Asset Management？ ◆ Publishers ◆ Any company publishing newspapers, magazines, catalogs or web sites. ◆ Content Creators ◆ Companies who create content for use in their business ◆ Broadcasters, Advertising Agencies, Studios, Sports Houses (NBA, NFL), Corporate Training Depts, Retailers ◆ Content Distributors ◆ Cable Operators, Telecoms, Internet Service Providers, Online Service Providers Who needs Media Asset Management？ ◆ There's a LOT of money being spent on this kind of technology, and not just by 'media' companies ◆ Retailers, for catalogs, web sites, call centers ◆ Chems/Pharms, for drug. discovery, knowledge management ◆ Legal, for document and knowledge management ◆ Federal, for video surveillance and knowledge management ◆ Manufacturing, for integration of CAD, text and business-to-business applications ◆ Anyone with a Web/Content Management challenge(omitted)

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권5호
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.