• 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.

• 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.

• 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.

• 한국공간정보시스템학회 논문지
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• 제11권2호
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• pp.119-132
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• 2009

GIS 기술의 발달로 많은 양의 공간 데이터가 축적됨에 따라 공간 데이터 마이닝의 중요성이 커지고 있다. 본 논문에서는 새로운 공간 데이터 마이닝 시스템 SD-Miner를 제안한다. SD-Miner는 크게 입력과 출력을 담당하는 사용자 인터페이스, 공간 데이터 마이닝 기능을 처리하는 데이터 마이닝 모듈, DBMS를 이용하여 데이터를 저장하고 관리하는 데이터 저장 모듈의 세 부분으로 구성된다. 특히, 데이터 마이닝 함수 모듈에서는 공간 데이터 마이닝의 주요 기법인 공간 클러스터링, 공간 분류, 공간 특성화, 시공 간 연관규칙 탐사 기능을 제공한다. SD-Miner는 다음과 같은 특징을 가진다. SD-Miner는 사용자로 하여 금 공간 데이터 마이닝뿐만 아니라 비 공간 데이터에 대한 마이닝도 가능하게 하며, 각 마이닝 함수들을 라이브러리 형태로 제공하기 때문에 다른 시스템에서도 쉽게 사용 가능하다. 또한, 마이닝 매개 변수들을 테이블의 형태로 입력받기 때문에 시스템의 범용성이 높다. 개발된 SD-Miner의 실용성을 규명하기 위하여 실제 공간 데이터를 이용한 데이터 마이닝을 수행함으로써 여러 가지 의미있는 결과를 도출한다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 춘계학술대회 논문집
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• pp.611-612
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• 2006

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.

• KIEAE Journal
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• 제12권2호
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• pp.77-82
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• 2012

Data mining is to discover problems in the large amounts of data. Also, data mining trying to find the cause of the problem and the structure. Building energy consumption patterns, the amount of data is infinite. Also, the patterns have a lot of direct and indirect effects. Discussion is needed about the correlation. This work looking for the cause of energy consumption. As a result, energy management can find out the issue. Building energy analysis utilizing data mining techniques to predict energy consumption. And the results are as follows: 1) Using data mining technique, We classified complicated data to several patterns and gained meaningful informations from them. 2) Using cluster analysis, We classified building energy consumption data of residents and analyzed characters of patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5321-5334
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• 2019

Data mining technology is frequently used in identifying the intention of users over a variety of information contexts. Since relevant terms are mainly hidden in text data, it is necessary to extract them. Quantification is required in order to interpret user preference in association with other structured data. This paper proposes rating and comments mining to identify user priority and obtain improved ratings. Structured data (location and rating) and unstructured data (comments) are collected and priority is derived by analyzing statistics and employing TF-IDF. In addition, the improved ratings are generated by applying priority categories based on materialized ratings through Sentiment-Oriented Point-wise Mutual Information (SO-PMI)-based emotion analysis. In this paper, an experiment was carried out by collecting ratings and comments on "place" and by applying them. We confirmed that the proposed mining method is 1.2 times better than the conventional methods that do not reflect priorities and that the performance is improved to almost 2 times when the number to be predicted is small.

• Journal of Information Processing Systems
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• 제6권4호
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• pp.521-536
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• 2010

Spatio-temporal patterns extracted from historical trajectories of moving objects reveal important knowledge about movement behavior for high quality LBS services. Existing approaches transform trajectories into sequences of location symbols and derive frequent subsequences by applying conventional sequential pattern mining algorithms. However, spatio-temporal correlations may be lost due to the inappropriate approximations of spatial and temporal properties. In this paper, we address the problem of mining spatio-temporal patterns from trajectory data. The inefficient description of temporal information decreases the mining efficiency and the interpretability of the patterns. We provide a formal statement of efficient representation of spatio-temporal movements and propose a new approach to discover spatio-temporal patterns in trajectory data. The proposed method first finds meaningful spatio-temporal regions and extracts frequent spatio-temporal patterns based on a prefix-projection approach from the sequences of these regions. We experimentally analyze that the proposed method improves mining performance and derives more intuitive patterns.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.221-232
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• 2021

In recent years, with the existence of huge amounts of data stored in huge databases, the need for developing accurate tools for analyzing data and extracting information and knowledge from the huge and multi-source databases have been increased. Hence, new and modern techniques have emerged that will contribute to the development of all other sciences. Knowledge discovery techniques are among these technologies, one popular technique of knowledge discovery techniques is data mining which aims to knowledge discovery from huge amounts of data. Such modern technologies of knowledge discovery will contribute to the development of all other fields. Data mining is important, interesting technique, and has many different and varied algorithms; Therefore, this paper aims to present overview of data mining, and clarify the most important of those algorithms and their uses.

• 한국정보시스템학회지:정보시스템연구
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• 제21권3호
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• pp.137-161
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• 2012

The increasing interest in big data analysis using various data mining techniques indicates that many commercial data mining tools now need to be equipped with fundamental text analysis modules. The most essential prerequisite for accurate analysis of text documents is an understanding of the exact semantics of each term in a document. The main difficulties in understanding the exact semantics of terms are mainly attributable to homonym and synonym problems, which is a traditional problem in the natural language processing field. Some major text mining tools provide a thesaurus to solve these problems, but a thesaurus cannot be used to resolve complex synonym problems. Furthermore, the use of a thesaurus is irrelevant to the issue of homonym problems and hence cannot solve them. In this paper, we propose a semantic text mining methodology that uses ontologies to improve the quality of text mining results by resolving the semantic ambiguity caused by homonym and synonym problems. We evaluate the practical applicability of the proposed methodology by performing a classification analysis to predict customer churn using real transactional data and Q&A articles from the "S" online shopping mall in Korea. The experiments revealed that the prediction model produced by our proposed semantic text mining method outperformed the model produced by traditional text mining in terms of prediction accuracy such as the response, captured response, and lift.