• 한국컴퓨터정보학회논문지
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• 제19권12호
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• pp.309-317
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• 2014

최근, 범죄가 증가함에 따라 범죄를 예측하고 예방하는 것은 사회의 중요한 이슈이며 정부 및 지자체는 다양한 방법론을 활용하여 범죄를 사전에 막고자 노력하고 있다. 데이터마이닝은 범죄예측 및 예방에 활용되는 대표적인 방법론이며, 범죄 패턴 분석, 범죄 발생 예측 등 다양한 분야에서 연구되고 있다. 그러나 데이터마이닝의 결과가 범죄학에서의 범죄 환경요소와 어떤 관련이 있는지 혹은, 사건해결에 어떤 도움을 줄 수 있는지에 대한 연구는 이루어지고 있지 않다. 따라서 본 논문에서는, 범죄학에서 범죄의 발생과 범죄 환경요소들의 상호 관련성을 보이고 범죄 발생과 관련된 환경요소와 데이터마이닝에 활용되는 변수 간의 관계를 정의하고자 하였다. 또한, 국내 보호관찰소에서 보관되고 있는 절도범죄 데이터를 사용하여 실제로 데이터마이닝의 결과가 범죄 환경요소와 어떤 관련이 있는지를 보이기 위해 군집분석을 적용하였다. 그 결과 각 군집별로 범죄가 발생하는 환경에 차이가 있었으며, 이를 활용하여 데이터마이닝이 범죄학관점에서 범죄 예측 및 예방 활용에 유의미함을 보였다.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.335-344
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• 2006

The purpose of this paper is to apply data mining method for the inbound call center optimization. Data mining analysis is come to be used in order to predict the degree of difficulty on the consultation. It is the method of maximal efficiency for the call center that uses of the predicted degree of difficulty and customer grade as routing which hits to the skill of the consultation unit. This method is to get the possibility of efficiency for the call center with the maximum efficiency.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.825-836
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• 2003

Based on customer information and financing processes in capital market, we derived individual models by applying multi-layered perceptrons, MDA, and decision tree. Further, the results from the existing single models were compared with the results from the integrated model that was developed using genetic algorithm. This study contributes not only to verifying the existing individual models and but also to overcoming the limitations of the existing approaches. We have depended upon the approaches that compare individual models and search for the best-fit model. However, this study presents a methodology to build an integrated data mining model using genetic algorithm.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.737-749
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• 2003

This Paper analyzed simultaneous activities of the time use survey by Korea National Statistical Office to use data mining's association rule. The survey of National Statistical Office in 1999 considered general analysis for main activities like that personal care(eating), employment and study, leisure, travel by purpose. But if we use the association rule, we can found the ratio of simultaneous activities at the same time. And also we can found the probability that another activities practise if we act one particular activity. Using this association rule of data mining we can do more developed and analytical sociological study.

• 경영과학
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• 제19권1호
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• pp.189-196
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• 2002

Data clustering is often one of the first steps in data mining analysis. It Identifies groups of related objects that can be used as a starling point for exploring further relationships. This technique supports the development of population segmentation models, such as demographic-based customer segmentation. This paper Purpose to present the development of two phase hierarchical clustering algorithm for group formation. Applications of the algorithm for product-customer group formation in customer relationahip management are also discussed. As a result of computer simulations, suggested algorithm outperforms single link method and k-means clustering.

• 융합정보논문지
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• 제10권7호
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• pp.14-19
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• 2020

확장성 보고서 언어인 XML기술을 회계보고 영역에 응용한 인터넷 표준인 XBRL에 기초한 회계정보처리 모형을 제안하고자 한다. 기업마다 문서의 특성이 상이하기에 의사결정자에게 유용한 정보를 제공하여야 한다는 회계의 목적에 비추어 그 중요성이 크다. 본 연구는 X-Hive 데이터베이스 내에 XBRL로 저장된 XML 계층구조를 기반으로 하는 데이터 마이닝 모형을 제안하고자 한다. 데이터마이닝 분석은 연관규칙으로 실험되었고 XBRL을 기반으로 DC-Apriori 데이터마이닝 방법을 Apriori알고리즘과 X쿼리를 결합하여 제안한다. 마지막으로 제안 모형의 타당성과 유효성에 대해서는 실험을 통해 검증하였다.

• 한국컴퓨터정보학회지
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• 제8권2호
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• pp.67-74
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• 2001

최근에 이용 가능한 데이터의 양이 폭발적으로 증가하고 있다 따라서 이들 데이터로부터 유용한 지식을 발견하는 자동화된 기법이 주목을 받고 있다. 데이터 마이닝이란 지식 발견의 중요한 단계로서, 데이터로부터 유용한 패턴을 발견하는 방법이다. 본 논문에서는 데이터 마이닝 기법을 조사한다 이러한 조사과정을 통하여 실세계에서 보다 효율적으로 적용 가능한 데이터 마이닝 기법을 찾아내고. 이들 기법에 대한 적절한 응용 영역과 앞으로의 연구방향을 제시한다.

• 한국정보시스템학회지:정보시스템연구
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• 제20권3호
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• pp.1-24
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• 2011

Recently, six sigma has been widely adopted in a variety of industries as a disciplined, data-driven problem solving approach or methodology supported by a handful of powerful statistical tools in order to reduce variation through continuous process improvement. Also, data mining has been widely used to discover unknown knowledge from a large volume of data using various modeling techniques such as neural network, decision tree, regression analysis, etc. This paper proposes a six sigma methodology based on data mining for effectively and efficiently processing massive data in driving six sigma projects. The proposed methodology is applied in the hot stove system which is a major energy-consuming process in a "P" steel company for improvement of heat efficiency through reduction of energy consumption. The results show optimal operation conditions and reduction of the hot stove energy cost by 15%.

• Journal of Information Technology Applications and Management
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• 제11권1호
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• pp.161-174
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• 2004

This study is intended to suggest1 the optimized data mining model for the efficient customer credit evaluation in the capital finance industry. To accomplish the research objective, various data mining models for the customer credit evaluation are compared and analyzed. Furthermore, existing models such as Multi-Layered Perceptrons, Multivariate Discrimination Analysis, Radial Basis Function, Decision Tree, and Logistic Regression are employed for analyzing the customer information in the capital finance market and the detailed data of capital financing transactions. Finally, the data from the integrated model utilizing a genetic algorithm is compared with those of each individual model mentioned above. The results reveals that the integrated model is superior to other existing models.

• International journal of advanced smart convergence
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• 제5권1호
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• pp.30-33
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• 2016

With the advent of big data, data mining is more increasingly utilized in various decision-making fields by extracting hidden and meaningful information from large amounts of data. Even as exponential increase of the request of unrevealing the hidden meaning behind data, it becomes more and more important to decide to select which data mining algorithm and how to use it. There are several mainly used data mining algorithms in biology and clinics highlighted; Logistic regression, Neural networks, Supportvector machine, and variety of statistical techniques. In this paper it is attempted to compare the classification performance of an exemplary algorithm J48 and REPTree of ML algorithms. It is confirmed that more accurate classification algorithm is provided by the performance comparison results. More accurate prediction is possible with the algorithm for the goal of experiment. Based on this, it is expected to be relatively difficult visually detailed classification and distinction.