• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.18-24
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• 2005

The methods of data mining are decision tree, association rules, clustering, neural network and so on. Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. We analyze Gyeongnam social indicator survey data by 2003 using association rule technique for environment information. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial and retail sectors. We can use association rule outputs in environmental preservation and environmental improvement.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.185-192
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• 2005

There are many data mining techniques such as association rule, decision tree, neural network analysis, clustering, genetic algorithm, bayesian network, memory-based reasoning, etc. We analyze 2003 Gyeongnam social indicator survey data using k-means clustering technique for environmental information. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper, we used k-means clustering of several clustering techniques. The k-means clustering is classified as a partitional clustering method. We can apply k-means clustering outputs to environmental preservation and environmental improvement.

• 경영과학
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• 제21권3호
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• pp.55-69
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• 2004

This study analyzes customer buying-behavior patterns in a department store as time goes on, and predicts moving patterns of its customers. Through them, it suggests in this paper short-term and long-term marketing promotion strategies. RFM techniques are utilized for customer segmentation. Customers are clustered by using the Kohonen's Self Organizing Map as a method of data mining techniques. Then C5.0, a decision tree analysis technique, is used to predict moving patterns of customers. Using real world data, this study evaluates the prediction accuracy of predictive models.

• 경영과학
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• 제19권2호
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• pp.205-220
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• 2002

As the business structure of many industries changes under IT progress and internet economy, the customer information has emerged a key factor in setting up the management policy. The customer has come to replace the product as a central figure in business competition. The domestic life insurance market has also experienced the rapid structural changes in IT time. The competition in the insurance industry to maintain the existing membership and to attract the new members gets stronger under such a new business circumstance. Accordingly, it is necessary for an individual insurance company to develop a systematic marketing plan, based on the customer information, to be competitive in the market. Unlike other studies in which customer characteristics are neglected, this study attempts to utilize the customer information by applying the data mining technique, and then suggests an efficient marketing strategy that could prevail in the competitive business environment.

• Communications for Statistical Applications and Methods
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• 제12권1호
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• pp.217-227
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• 2005

The data mining is a new approach to extract useful information through effective analysis of huge data in numerous fields. We utilized this data mining technique to analyze medical record of 35,671 people. Whole data were assorted by BMI score and divided into two groups. We tried to find out BMI risk factor from overweight group by analyzing the raw data with data mining approach. The result extracted by C5.0 decision tree method showed that important risk factors for BMI score are triglyceride, gender, age and HDL cholesterol. Odds ratio of major risk factors were calculated to show individual effect of each factors.

• Journal of Information Technology Applications and Management
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• 제15권1호
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• pp.225-242
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• 2008

PM (Inconsistency Pattern Modeling) is a hybrid supervised learning technique using the inconsistence pattern of input variables in mining data sets. The IPM tries to improve prediction accuracy by combining more than two different supervised learning methods. The previous related studies have shown that the IPM was superior to the single usage of an existing supervised learning methods such as neural networks, decision tree induction, logistic regression and so on, and it was also superior to the existing combined model methods such as Bagging, Boosting, and Stacking. The objectives of this paper is explore the characteristics of the IPM. To understand characteristics of the IPM, three experiments were performed. In these experiments, there are high performance improvements when the prediction inconsistency ratio between two different supervised learning techniques is high and the distance among supervised learning methods on MDS (Multi-Dimensional Scaling) map is long.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2006년도 춘계공동학술대회 논문집
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• pp.265-270
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• 2006

In an organization where major business operations are geared by business process management system(BPMS), routine tasks are processed according to the predefined business processes. However, most business operations are subject to some sort of exceptions, and the exceptional situations require update of the existing business process model, or a new business process model has to be defined to handle the exceptions. This paper proposes a system architecture that deploys business process repository as the media for storage and retrieval of the various business process models developed for exception handling. Well defined situation variables and decision variables play the key role for efficient storage and retrieval of the business process models developed for exception handling. The data mining technique C5.0 was used to build the optimum path for the process repository search tree.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2004년도 추계학술대회
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• pp.115-124
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• 2004

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are association rules, decision tree, clustering, neural network and so on. Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2004년도 추계학술대회 및 정기총회
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• pp.395-398
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• 2004

It is effective and desirable for a proper customer relational management(CRM) to send an email of product sales' advertisement bills for the prospective customers rather than to send spam mails for non specific customers. This study identifies the prospective customers with high probability to buy the specific products using Artificial Neural Network(ANN) and Induction Rule(IR) technique. We suggest an integrated model, IRANN of ANN and IR of decision tree program C5.0 and, also compare and analyze the accuracy of ANN, IR, and IRANN each other.

• 대한전기학회논문지:전력기술부문A
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• 제52권9호
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• pp.513-520
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• 2003

Service restoration is an emergency control in distribution constrol centers to restore out-of-service area as soon as possible when a fault occurs in distribution networks. therefore, it requires fast computation time and high quality solutions for load balancing. In this paper. a load balance index and heuristic guided best-first search are proposed for these problem. The proposed algorithm consists of two parts. One is to set up a decision tree to represent the various switching operations available. Another is to identify the most effective the set of switches using proposed search technique and a load balance index. Test results on the KEPCO's 108 bus distribution system show that the performance is efficient and robust.