• 한국지능시스템학회논문지
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• 제13권3호
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.247-250
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• 2003

Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

• 한국지능시스템학회논문지
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• 제19권4호
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• pp.525-531
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• 2009

데이터 마이닝(Data Mining)은 수집된 데이터로 부터 감춰진 패턴을 찾는 작업이다. 여기에서 수집된 데이터는 예측 및 추천을 위한 기반 정보로 중요한 역할을 하며, 분석 결과의 성능을 향상시키기 위해 잘못된(Missing value) 데이터를 선별하는 과정을 필요로 한다. 수집한 데이터에서 의도하지 못한 데이터를 선별하기 위한 기존의 방법은 주로 통계적이거나 단순 거리(Distance)에 기반을 둔 방법을 이용하였다. 하지만 환경 및 데이터의 특성을 고려하지 못하여, 의미 있는 데이터도 함께 분석에서 제외 될 수 있는 문제점을 가지고 있다. 본 논문은 인간의 경험적 지식을 수집된 데이터와 비교하여 가중치로 변환하고, 의사결정트리(Decision Tree)의 생성에 이용한다. 생성된 트리는 인간의 지식이 반영되어 기존의 분석 방법보다 신뢰성이 높다고 할 수 있으며, 실험을 통하여 제안하는 방법의 유효성을 확인하였다.

• Asia pacific journal of information systems
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• 제25권1호
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• pp.83-97
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• 2015

Recently, the exhibition industry has developed rapidly with the development of information technologies. Most exhibitors in an exhibition plan and deploy many events that may provide advantages to visitors as a method of effective promotion. The growth and propagation of wireless technologies is a powerful marketing tool for exhibitors. However, exhibitors still rely on domain experts who are costly and time consuming because of the manual knowledge input procedure. Moreover, it is prone to biases and errors and not suitable for managing fast-growing and tremendous amounts of data that far exceed a human's ability to comprehend. To overcome these problems, data mining technology may be a great alternative, but it needs to be fit to each exhibition. This study uses data mining technology with the Predictive Model Markup Language (PMML) to suggest a system that supports intelligent services and that improves stakeholder satisfaction. This system provides advantages to the exhibitor, show organizer, and system designer, and is first enhanced by integrating data mining technologies through the knowledge of exhibition experts. Second, using the PMML, the system can automate the process of applying data mining models to solve real-time processing problems in the exhibition environment.

• 지능정보연구
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• 제11권3호
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• pp.57-67
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• 2005

대학의 학사관리 시스템은 학생이 입학하여 졸업하기까지 수행하는 여러 가지 학사활동 및 과외활동으로부터 발생하는 방대한 데이터를 보유하고 있다. 그러나 이들을 학생들의 전공지도나 진로지도에 효과적으로 활용하지 못하고 있다. 본 논문에서는 학사관리 시스템에 축적된 정보를 대상으로 학생들의 전공선택 및 진로지도에 도움을 줄 수 있는 새로운 정보와 지식을 생성하는 방법을 개발, 제시하였다. 특히, 요인분석, 계층분석 (AHP) 기법을 동원하여 데이터 마이닝을 수행함으로써 유용한 지식과 규칙을 생성하였다. 방법론에 사용할 기본 자료는 학생들의 Holland 적성검사 결과이다. 연구의 결과로서 기존의 학생지도 담당자가 수작업으로는 알아낼 수 없었던 학생지도에 관한 유용한 규칙을 도출할 수 있었다.

• 인터넷정보학회논문지
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• 제1권1호
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• pp.63-72
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• 2000

데이터마이닝은 방대한 데이터 자료로부터 숨어있는 지식이나 유용한 정보를 추출하는 과정이다. 이러한 데이터 마이닝 알고리즘은 통계학, 전자계산학, 그리고 기계학습 분야에서의 오랜 기간동안 이루어진 연구 결과의 산물이다. 어느 특정한 상황에 적용하는 특정한 기술들의 선택은 구현되어야 하는 데이터 마이닝 임무의 성격과 가용한 데이터의 성격에 의존한다. 데이터 마이닝에는 여러 임무가 있으며, 그 중에서 가장 대표적인 임무가 분류라고 (classification) 볼 수 있다. 분류는 인간 사고의 기본적인 요소이기 때문에 여러 응용 분야에서 많은 연구가 진행되어 왔으며, 문제 분석의 첫 단계라고 볼 수 있다. 본 논문에서는 학습문제에서 강건성(robust)을 갖는 유전자 알고리즘 기반의 분류시스템을 제안하고, 데이터 마이닝에서 중요한 분류기능에 관련된 문제인 nDmC에 응용해서 그 유효성을 검증한다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2005년도 춘계학술대회
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• pp.266-273
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• 2005

대학의 학사관리 시스템은 학생이 입학하여 졸업하기까지 수행하는 여러 가지 학사활동 및 과외활동으로부터 발생하는 방대한 데이터를 보유하고 있다. 그러나 이들을 학생들의 전공지도나 진로지도에 효과적으로 활용하지 못하고 있다. 본 논문에서는 학사관리 시스템에 축적된 정보를 대상으로 데이터 마이닝 기법을 적용하여 학생들의 전공선택 및 진로지도에 도움을 줄 수 있는 새로운 정보와 지식을 생성하는 방법을 개발, 제시하였다. 이 연구를 위하여 요인분석, 계층분석 (AHP), 인공신경망, CART 기법 등을 동원하여 데이터 마이닝을 수행함으로써 유용한 지식과 규칙을 생성하였다. 방법론의 개발에 사용된 기본 자료들은 학생들의 Holland 적성검사, TOEIC 점수, 이수과목, 평점 등이다. 연구의 결과로서 기존의 학생지도 담당자가 수작업으로는 알아낼 수 없었던 학생지도에 관한 유용한 규칙을 도출할 수 있었다.

• 한국의류학회지
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• 제46권6호
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• pp.1142-1160
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• 2022

Text data plays a significant role in understanding and analyzing trends in consumer, business, and social sectors. For text analysis, there must be a corpus that reflects specific domain knowledge. However, in the field of fashion, the professional corpus is insufficient. This study aims to develop a taxonomy and thesaurus that considers the specialty of fashion products. To this end, about 100,000 fashion vocabulary terms were collected by crawling text data from WSGN, Pantone, and online platforms; text subsequently was extracted through preprocessing with Python. The taxonomy was composed of items, silhouettes, details, styles, colors, textiles, and patterns/prints, which are seven attributes of clothes. The corpus was completed through processing synonyms of terms from fashion books such as dictionaries. Finally, 10,294 vocabulary words, including 1,956 standard Korean words, were classified in the taxonomy. All data was then developed into a web dictionary system. Quantitative and qualitative performance tests of the results were conducted through expert reviews. The performance of the thesaurus also was verified by comparing the results of text mining analysis through the previously developed corpus. This study contributes to achieving a text data standard and enables meaningful results of text mining analysis in the fashion field.

• 한국경영과학회지
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• 제28권3호
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• pp.31-47
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• 2003

Quality design in practice highly depends on human designer's intuition and past experiences due to lack of formal knowledge about the relationship among 10 variables. This paper represents an data mining approach for developing quality design support system that integrates Case Based Reasoning (CBR) and Artificial Neural Networks (ANN) to effectively support all the steps in quality design process. CBR stores design cases in a systematic way and retrieve them quickly and accurately. ANN predicts the resulting quality attributes of design alternatives that are generated from CBR's adaptation process. When the predicted attributes fail to meet the target values, quality design simulation starts to further adapt the alternatives to the customer's new orders. To implement the quality design simulation, this paper suggests (1) the data screening method based on ξ-$\delta$ Ball to obtain the robust ANN models from the large production data bases, (2) the procedure of quality design simulation using ANN and (3) model management system that helps users find the appropriate one from the ANN model base. The integration of CBR and ANN provides quality design engineers the way that produces consistent and reliable design solutions in the remarkably reduced time.