• The KIPS Transactions:PartD
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• v.18D no.3
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• pp.169-178
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• 2011

Recently, data sharing between enterprises or organizations is required matter for task cooperation. In this process, when the enterprise opens its database to the affiliates, it can be occurred to problem leaked sensitive information. To resolve this problem it is needed to hide sensitive information from the database. Previous research hiding sensitive information applied different heuristic algorithms to maintain quality of the database. But there have been few studies analyzing the effects on the items modified during the hiding process and trying to minimize the hided items. This paper suggests eFP-Tree(Extended Frequent Pattern Tree) based FP-Tree(Frequent Pattern Tree) to hide sensitive frequent itemsets. Node formation of eFP-Tree uses border to minimize impacts of non sensitive frequent itemsets in hiding process, by organizing all transaction, sensitive and border information differently to before. As a result to apply eFP-Tree to the example transaction database, the lost items were less than 10%, proving it is more effective than the existing algorithm and maintain the quality of database to the optimal.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1323-1334
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• 2011

This paper suggests the border based HSFI algorithm to hide sensitive frequent itemsets. Node formation of FP-Tree which is different from the previous one uses the border to minimize the impacts of nonsensitive frequent itemsets in hiding process, including the organization of sensitive and border information, and all transaction as well. As a result of applying HSFI algorithms, it is possible to be the example transaction database, by significantly reducing the lost items, it turns out that HSFI algorithm is more effective than the existing algorithm for maintaining the quality of more improved database.

• Journal of Korea Multimedia Society
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• v.8 no.9
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• pp.1163-1176
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• 2005

The Web is the largest distributed information space but, the individual's capacity to read and digest contents is essentially fixed. In these Web environments, mining traversal patterns is an important problem in Web mining with a host of application domains including system design and information services. Conventional traversal pattern mining systems use the inter-pages association in sessions with only a very restricted mechanism (based on vector or matrix) for generating frequent K-Pagesets. We extend a family of novel algorithms (termed WebPR - Web Page Recommend) for mining frequent traversal patterns and then pageset to recommend. We add a WebPR(A) algorithm into a family of WebPR algorithms, and propose a new winWebPR(T) algorithm introducing a window concept on WebPR(T). Including two extended algorithms, our experimentation with two real data sets, including LadyAsiana and KBS media server site, clearly validates that our method outperforms conventional methods.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.187-198
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• 2004

The World-Wide Web is the largest distributed Information space and has grown to encompass diverse information resources. However, although Web is growing exponentially, the individual's capacity to read and digest contents is essentially fixed. From the view point of Web users, they can be confused by explosion of Web information, by constantly changing Web environments, and by lack of understanding needs of Web users. In these Web environments, mining traversal patterns is an important problem in Web mining with a host of application domains including system design and Information services. Conventional traversal pattern mining systems use the inter-pages association in sessions with only a very restricted mechanism (based on vector or matrix) for generating frequent k-Pagesets. We develop a family of novel algorithms (termed WebPR - Web Page Recommend) for mining frequent traversal patterns and then pageset to recommend. Our algorithms provide Web users with new page views, which Include pagesets to recommend, so that users can effectively traverse its Web site. The main distinguishing factors are both a point consistently spanning schemes applying inter-pages association for mining frequent traversal patterns and a point proposing the most efficient tree model. Our experimentation with two real data sets, including Lady Asiana and KBS media server site, clearly validates that our method outperforms conventional methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.233-236
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• 2008

최근 컴퓨터와 통신의 기술이 빠르게 발달함에 따라 사회 각 부분은 그동안 경험하지 못했던 정보화라는 새로운 변화를 겪었다. 그 결과 정보화 수준이 점점 고도화 될수록 더욱 다양하고 방대한 데이터가 생성되어 데이터베이스를 이루게 되었다. 방대한 데이터에서 유용한 정보를 얻는 데이터마이닝 기법이 중요한 문제로 대두되었다. 데이터마이닝 기법은 점점 더 많은 분야에서 합리적인 선택을 위해 필수적으로 사용된다. 본 논문은 마이닝 기법을 적용하여 방대한 데이터베이스가 최적의 여행 경로 선택을 제공한다. 본 논문은 빈발 패턴 증가 기법에 가중치를 두어 여행자가 여행지를 선별하기 좋은 환경을 제공한다. 미래 산업 중 가장 중요한 산업 중 하나인 관광 산업은 계속적으로 성장하고 있으며 논문에서 제시하는 데이터 마이닝 기법으로 더 큰 발전을 기대한다.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.65-78
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• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.