• 한국지능시스템학회논문지
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• 제14권6호
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

• 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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• 제24권63호
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• pp.23-31
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• 2001

Data mining can be either directed or undirected. One way of thinking about it is that we use undirected data mining to recognize relationship in the data and directed data mining to explain those relationships once they have been found. Several data mining techniques have received considerable research attention. In this paper, we propose an algorithm for discovering association rules as directed data mining and applying them to classification. In the first phase, we find frequent closed itemsets and association rules. After this phase, we construct the decision trees using discovered association rules. The algorithm can be applicable to customer relationship management.

• 한국정보기술학회 영문논문지
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• 제9권2호
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• pp.89-97
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• 2019

Recently, there is an increasing demand for applications utilizing maps and locations such as autonomous vehicles and location-based services. Since these applications are developed based on spatial data, interest in spatial data processing is increasing and various studies are being conducted. In this paper, I propose a parallel mining algorithm using the CUDA library to efficiently analyze large spatial data. Spatial data includes both geometric (spatial) and non-spatial (aspatial) attributes. The proposed parallel spatial data mining algorithm analyzes both the geometric and non-spatial relationships between two layers. The experiment was performed on graphics cards containing CUDA cores based on TIGER/Line data, which is the actual spatial data for the US census. Experimental results show that the proposed parallel algorithm using CUDA greatly improves spatial data mining performance.

• 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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 V
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• pp.2561-2564
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• 2003

Data mining is an effective method of the discovery of useful information such as rules and previously unknown patterns existing in large databases. The discovery of association rules is an important data mining problem. We have developed a new parallel mining called Distributed Frequent Pattern Tree (abbreviated by DFPT) algorithm on a distributed shared nothing parallel system to detect association rules. DFPT algorithm is devised for parallel execution of the FP-growth algorithm. It needs only two full disk data scanning of the database by eliminating the need for generating the candidate items. We have achieved good workload balancing throughout the mining process by distributing the work equally to all processors. We implemented the algorithm on a PC cluster system, and observed that the algorithm outperformed the Improved Count Distribution scheme.

• 산업경영시스템학회지
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• 제23권54호
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• pp.27-36
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• 2000

The problem of discovering association rules is an emerging research area, whose goal is to extract significant patterns or interesting rules from large databases and several algorithms for mining association rules have been applied to item-oriented sales transaction databases. Data warehouses and OLAP engines are expected to be widely available. OLAP and data mining are complementary; both are important parts of exploiting data. Our study shows that data cube is an efficient structure for mining association rules. OLAP databases are expected to be a major platform for data mining in the future. In this paper, we present an efficient and effective algorithm for mining association rules using data cube. The algorithm can be applicable to enhance the power of competitiveness of business organizations by providing rapid decision support and efficient database marketing through customer segmentation.

• 한국정보전자통신기술학회논문지
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• 제12권6호
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• pp.681-689
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• 2019

웹 사용 패턴 발견은 웹 로그 데이터를 사용하는 고급 수단이며 웹 로그 데이터 마이닝에 데이터 마이닝 기술을 적용한 특정 응용이다. 교육 분야에서 데이터 마이닝 (DM)은 데이터 마이닝 기술을 교육 데이터 (대학의 웹 로그, e-러닝, 적응형 하이퍼미디어 및 지능형 튜터링시스템 등)에 적용한다. 따라서 교육 연구 문제를 해결하기 위해 이러한 유형의 데이터를 분석하는 것이 목표이다. 본 논문에서는 대학의 웹 로그 데이터가 데이터 마이닝의 연구 대상으로 사용되어 진다. 데이터베이스 OLAP 기술을 사용하여 웹 로그 데이터가 데이터 마이닝에 사용될 수 있는 데이터 형식으로 사전 처리되고 그 처리 결과가 MSSQL에 저장된다. 동시에 처리 된 웹 로그 레코드를 기반으로 기본 데이터 통계 및 분석이 완료된다. 또한 웹 사용 패턴 마이닝의 Apriori Algorithm 및 구현 프로세스를 소개하고 Python 개발 환경에서 Apriori Algorithm 프로그램을 개발했다. 그런 다음 Apriori Algorithm의 성능을 보이고 웹 사용자 액세스 패턴의 마이닝을 실현했다. 이 연구 결과는 교육 시스템 개발에 패턴을 적용하는데 중요한 이론적 의미를 갖는다. 다음 연구로는 분산 컴퓨팅 환경에서 Apriori Algorithm의 성능 향상을 연구하는 것이다.

• 산업경영시스템학회지
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• 제28권4호
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• pp.94-101
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• 2005

This paper presents a data-mining aided heuristic algorithm development. The developed algorithm includes three steps. The steps are a uniform selection, development of feature functions and clustering, and a decision tree making. The developed algorithm is employed in designing an optimal multi-station fixture layout. The objective is to minimize the sensitivity function subject to geometric constraints. Its benefit is presented by a comparison with currently available optimization methods.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.455-465
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• 2006

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, and relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze industrial waste database using data mining technique. We use k-means algorithm for clustering and C5.0 algorithm for decision tree and Apriori algorithm for association rule. We can use these outputs for environmental preservation and environmental improvement.