• 품질경영학회지
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• 제34권1호
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• pp.13-19
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• 2006

Data mining is a useful tool for analyzing data from different perspectives and for summarizing them into useful information. Recently, the data mining methods are applied to solving quality problems of the manufacturing processes. This paper discusses the problems of construction of a quality mining system, which is based on the various data mining methods. The quality mining system includes recipe optimization, significant difference test, finding critical processes, forecasting the yield. The contents and system of this paper are focused on the TFT-LCD manufacturing process. We also provide some illustrative field examples of the quality mining system.

• Journal of Information Technology Applications and Management
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• 제24권1호
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• pp.33-43
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• 2017

The content curation service through big data analysis is receiving great attention in various content fields, such as film, game, music, and book. This service recommends personalized contents to the corresponding user based on user's preferences. The existing book curation systems recommended books to users by using bibliographic citation, user profile or user log data. However, these systems are difficult to recommend books related to character names or spatio-temporal information in text contents. Therefore, in this paper, we suggest a personalized book curation system based on integrated mining of a book. The proposed system consists of mining system, recommendation system, and visualization system. The mining system analyzes book text, user information or profile, and SNS data. The recommendation system recommends personalized books for users based on the analysed data in the mining system. This system can recommend related books using based on book keywords even if there is no user information like new customer. The visualization system visualizes book bibliographic information, mining data such as keyword, characters, character relations, and book recommendation results. In addition, this paper also includes the design and implementation of the proposed mining and recommendation module in the system. The proposed system is expected to broaden users' selection of books and encourage balanced consumption of book contents.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2006년도 춘계학술대회
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• pp.142-148
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• 2006

Data mining is a useful tool for analyzing data from different perspectives and for summarizing them into useful information. Recently, the data mining methods are applied to solving quality problems of the manufacturing processes. This paper discusses the problems of construction of a quality mining system, which is based on the various data mining methods. The quality mining system includes recipe optimization, significant difference test, finding critical processes, forecasting the yield. The contents and system of this paper are focused on the TFT-LCD manufacturing process. We also provide some illustrative field examples of the quality mining system.

• 인지과학
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• 제13권3호
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• pp.33-46
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• 2002

최근 인터넷에는 기존의 데이터베이스 형태가 아닌 일정한 구조를 가지지 않았지만 상당한 잠재적 가치를 지니고 있는 텍스트 데이터들이 많이 생성되고 있다. 고객창구로서 활용되는 게시판이나 이메일, 검색엔진이 초기 수집한 데이터 둥은 이러한 비구조적 데이터의 좋은 예이다. 이러한 텍스트 문서의 분류를 위하여 각종 텍스트마이닝 도구가 개발되고 있으나, 이들은 대개 단순한 통계적 방법에 기반하고 있기 때문에 정확성이 떨어지고 좀 더 다양한 데이터마이닝 기법을 활용할 수 있는 방법이 요구되고 있다. 그러나, 정형화된 입력 데이터를 요구하는 데이터마이닝 기법을 텍스트에 직접 적용하기에는 많은 어려움이 있다. 본 연구에서는 이러한 문제를 해결하기 위하여 전처리 과정에서 텍스트마이닝을 수행하고 정제된 중간결과를 데이터마이닝으로 처리하여 텍스트마이닝에 피드백 시켜 정확성을 높이는 방법을 제안하고 구현하여 보았다. 그리고, 그 타당성을 검증하기 위하여 유해사이트의 웹 컨텐츠를 분류해내는 작업에 적용하여 보고 그 결과를 분석하여 보았다. 분석 결과, 제안방법은 기존의 텍스트마이닝만을 적용할 때에 비하여 오류율을 현저하게 줄일 수 있었다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2004년도 추계학술대회 및 정기총회
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• pp.399-405
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• 2004

With the explosive growth of information sources available under various information technology and business environment, it has become increasingly necessary for determining effective marketing strategies and optimizing the logical structure of the CRM data mining system. In this paper, we present an overview of the data mining for strategy focused CRM structure. This includes preprocessing, transaction identification and data integration components. We describe the main part of this paper to the discussion of processes and problems that characterize the mining tools and techniques, identify the CRM data mining, and provide a general architecture of a system to do focused CRM data mining that require further research and development.

• 한국산학기술학회논문지
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• 제12권1호
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• pp.459-466
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• 2011

이 논문에서는 침입 탐지시스템의 체계적인 경보데이터관리 및 경보데이터 상관관계 분석을 위하여 데이터 마이닝 기법을 적용한 경보 데이터 마이닝 프레임워크를 제안한다. 적용된 마이닝 기법은 속성기반 연관규칙, 속성기반 빈발에피소드, 오경보 분류, 그리고 순서기반 클러스터링이다. 이들 구성요소들은 각각 대량의 경보 데이터들로부터 알려지지 않은 패턴을 탐사하여 공격시나리오를 유추하거나, 공격 순서를 예측하는 것이 가능하며, 데이터의 그룹화를 통해 고수준의 의미를 추출할 수 있게 해준다. 실험 및 평가를 위하여 제안된 경보데이터 마이닝 프레임워크의 프로토타입을 구축하였으며 프레임워크의 기능을 검증하였다. 이 논문에서 제안한 경보 데이터 마이닝 프레임워크는 기존의 경보데이터 상관관계분석에서는 해결하지 못했던 통합적인 경보 상관관계 분석 기능을 수행할 뿐만 아니라 대량의 경보데이터에 대한 필터링을 수행하는 장점을 가진다. 또한 추출된 규칙 및 공격시나리오는 침입탐지시스템의 실시간 대응에 활용될 수 있다.

• 지능정보연구
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• 제8권2호
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• pp.159-175
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• 2002

웹 마이닝은 e-Business 환경하에서 존재하는 대량의 웹 데이터에 데이터 마이닝 기법을 적용하여 유용하고 이해 가능한 정보를 추출해내는 과정을 의미하는데, 성공적인 e-Business전개를 위한 핵심적인 기술이다. 본 논문은 인공지능 기법에 기반한 웹마이닝 기술을 활용하여 e-Business상의 온라인 고객의 특성을 분석할 수 있는 data visualization system과 구매 판매 예측시스템의 효과적인 구조와 핵심적인 분석절차를 제안하였다.

• 한국CDE학회논문집
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• 제14권6호
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• pp.382-389
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• 2009

Recently, knowledge management has been required in companies as a tool of competitiveness. Companies have constructed Enterprise Resource Planning(ERP) system in order to manage huge knowledge. But, it is not easy to formalize knowledge in organization. We focused on data mining system by genetic programming(GP). Data mining system by genetic programming can be useful tools to derive and extract the necessary information and knowledge from the huge accumulated data. However when we don't have enough amounts of data to perform the learning process of genetic programming, we have to reduce input parameter(s) or increase number of learning or training data. In this study, an enhanced data mining method combining Genetic Programming with Self organizing map, that reduces the number of input parameters, is suggested. Experiment results through a prototype implementation are also discussed.

• 산업경영시스템학회지
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• 제31권2호
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• pp.104-111
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• 2008

A process mining is considered to support the discovery of business process for unstructured process model, and a process mining algorithm by using the associated rule and sequence pattern of data mining is developed to extract information about processes from event-log, and to discover process of alternative, concurrent and hidden activities. Some numerical examples are presented to show the effectiveness and efficiency of the algorithm.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.135-146
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• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.