• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.361-363
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• 2001

Although data mining promises a new paradigm to discover medical knowledge form a database, there are many problems to be solved before real application is feasible. We had the chance to provide a data set to be analyzed as a discovery challenge by using various data mining techniques at the PKDD conference. As data providers, we evaluated and discussed results and clarified problems.

• 대한안전경영과학회지
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• 제24권4호
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• pp.149-156
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• 2022

Manufacturing process mining performs various data analyzes of performance on event logs that record production. That is, it analyzes the event log data accumulated in the information system and extracts useful information necessary for business execution. Process data analysis by process mining analyzes actual data extracted from manufacturing execution systems (MES) to enable accurate manufacturing process analysis. In order to continuously manage and improve manufacturing and manufacturing processes, there is a need to structure, monitor and analyze the processes, but there is a lack of suitable technology to use. The purpose of this research is to propose a manufacturing process analysis method using process mining and to establish a manufacturing process mining system by analyzing empirical data. In this research, the manufacturing process was analyzed by process mining technology using transaction data extracted from MES. A relationship model of the manufacturing process and equipment was derived, and various performance analyzes were performed on the derived process model from the viewpoint of work, equipment, and time. The results of this analysis are highly effective in shortening process lead times (bottleneck analysis, time analysis), improving productivity (throughput analysis), and reducing costs (equipment analysis).

• Journal of information and communication convergence engineering
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• 제10권1호
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• pp.40-44
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• 2012

Data mining, which is attracting public attention, is a process of drawing out knowledge from a large mass of data. The key technique in data mining is the ability to maximize the similarity in a group and minimize the similarity between groups. Since grouping in data mining deals with a large mass of data, it lessens the amount of time spent with the source data, and grouping techniques that shrink the quantity of the data form to which the algorithm is subjected are actively used. The current grouping algorithm is highly sensitive to static and reacts to local minima. The number of groups has to be stated depending on the initialization value. In this paper we propose a gene algorithm that automatically decides on the number of grouping algorithms. We will try to find the optimal group of the fittest function, and finally apply it to a data mining problem that deals with a large mass of data.

• 산업경영시스템학회지
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• 제25권3호
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• pp.58-63
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• 2002

The Internet, as a commercial tool, presented a new market that connects producers with consumers through the E-commerce. Now, E-commerce spreads over almost all industries through the Internet excluding some. This research indicates the reason why the E-commerce is not activated in agricultural Industry, which is less developed than other industries. And it suggests a good example of E-commerce on the agricultural products combining on and off line markets. In addition, data-mining technique is suggested to analyze whole information in system.

• 신재생에너지
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• 제18권2호
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• pp.18-25
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• 2022

In this study, we propose a wind power generation prediction system that applies machine learning and data mining to predict wind power generation. This system increases the utilization rate of new and renewable energy sources. For time-series data, the data set was established by measuring wind speed, wind generation, and environmental factors influencing the wind speed. The data set was pre-processed so that it could be applied appropriately to the model. The prediction system applied the CNN (Convolutional Neural Network) to the data mining process and then used the LSTM (Long Short-Term Memory) to learn and make predictions. The preciseness of the proposed system is verified by comparing the prediction data with the actual data, according to the presence or absence of data mining in the model of the prediction system.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.125-133
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• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• 정보처리학회논문지D
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• 제9D권3호
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• pp.365-380
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• 2002

시간 데이타마이닝은 기존 데이타마이닝에 시간 개념을 추가하여 "시간값을 가진 대용량 데이타로부터 이전에 잘 알려지지는 않았지만, 묵시적이고 잠재적으로 유용한 시간 지식을 탐사하는 기술"로 정의된다. 시간 지식이란 주기적 패턴, 캘린더 패턴, 경향 등과 같이 시간 의미와 시간 관계를 가진 지식을 말한다. 실세계에서는 환자의 병력, 상품 구매 이력, 웹 로그 등과 같은 다양한 시간 데이타가 존재하며 이로부터 여러 형태의 유용한 시간 지식을 찾아낼 수 있다. 데이타마이닝에 대한 연구가 진행되면서 순차 패턴, 유사 시계열 탐사, 주기적 연관규칙 탐사 등과 같이 시간 지식을 탐사하고자 하는 시간 데이타마이닝에 대한 부분적인 연구가 수행되었다. 그러나 기존 연구는 단순히 데이타의 발생 순서 및 유사한 패턴을 찾아내는데 중점을 두고 있어 데이타가 포함하고 있는 시간 의미와 시간 관계를 탐사하는데 부족하며, 시간 지식의 전체적인 측면보다는 연관 규칙과 같은 일부분만을 다루고 있다는 문제점을 가지고 있다. 따라서 이 논문에서는 시간 데이타마이닝에 대한 체계적인 연구를 위하여 시간 데이타마이닝에 대한 기존 연구 내용과 해결해야 할 문제점을 분석하고 이를 바탕으로 전체적인 프레임워크를 제시하였다. 또한 그 구현 방안 및 적용평가를 수행하였다. 프레임워크에서는 시간 데이타마이닝 모델을 제안하고, 이를 바탕으로 시간 데이타마이닝 질의어와 시간 지식을 탐사할 수 있는 시간 데이타마이닝 시스템을 설계하였다.

• 한국군사과학기술학회지
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• 제20권4호
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• pp.551-557
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• 2017

Commanders want to receive quickly scientific and analytic results about the battlefield situation. Unfortunately, decision support system of Army Tactical Command Information System(ATCIS) is restricted to message procedures and searching function based on manual work. In this paper, we propose applying Data Mining to ATCIS for supporting rapid decision making based on the scientific and analytic method. The purpose of this proposal is to efficiently execute the tactical planning and employment of the subordinate units in order to achieve the mission.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제8권5호
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• pp.489-500
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• 2002

비즈니스 인텔리젼스에 대한 관심이 증대되면서 핵심 기술로써 데이타마이닝의 적용이 증대되고 있다. e-Business에서의 비즈니스 인텔리젼스를 지원하기 위해 다양한 마이닝 연산을 통합적으로 제공하는 마이닝 시스템은 데이타베이스 시스템과 유연하게 통합될 수 있어야 하며, 또한 다양한 비즈니스 응용에서의 마케팅 프로세스를 쉽게 구현할 수 있는 인터페이스를 제공하여야 한다. 이 연구에서는 e-Business영역에서의 BI를 지원하기 위해 데이타마이닝 기법을 통합적으로 제공하는 시스템으로써 EC-DaMiner 시스템을 설계, 구현하였다. 데이타마이닝 시스템은 기존의 데이타베이스 시스템과의 표준적인 인터페이스를 통하여 연동될 수 있도록 하였다. 아울러 비즈니스 어플리케이션들은 마이닝 질의어인 MQL을 통하여 규칙을 탐사하고 탐사된 규칙을 기존의 마케팅 데이타베이스에 모델화하여 반영함으로써 마케팅 전략의 구현을 용이하게 하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 A
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• pp.214-216
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• 2001

Data Mining is a issue of Database fields. Data mining is discovered optimally interesting rules for user, which are results of specific requirements of user. through past data. Through to analyze and to statical suppose interesting rules. we can prepare future faults of system. In this paper, we present a new way which is discovered and repaired faults of Electric Power system using Data Mining techniques.