• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.655-657
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• 2002

데이터 마이닝 기술 중 하나인 연관규칙 마이닝의 병렬 알고리즘들은 동형질의 병렬 컴퓨팅 시스템을 대상으로 하여 개발되었다. 그러나, 이러한 병렬 알고리즘들은 클러스터 시스템 또는 Network Of Workstation(NOW)과 같은 저가의 프로세서들로 구성된 집합적인 병렬 컴퓨팅 시스템에서는 부적당하다. 이는 이들 시스템이 다른 성능을 가진 프로세서로 구성되어 있거나 여러 사용자의 접근을 허용하는 등의 이형성을 가지기 때문이다. 결과적으로 이러한 환경을 고려하지 않은 기존의 병렬 연관규칙 알고리즘들은 전체 시스템의 성능을 활용하지 못하게 되어 성능저하를 피할 수 없다. 본 논문에서는 대표적인 병렬 연관규칙 알고리즘인 Data Distribution 알고리즘을 위만 효과적이고 확장성 있는 동적 부하분산 알고리즘의 설계와 구현을 다룬다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.100-102
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• 2002

데이터 마이닝의 대표적인 기법인 연관규칙 마이닝을 위한 다양만 알고리즘들이 제안되었고, 각 알고리즘에 따른 대용량 데이터에 대한 신속한 탐색을 위한 독특한 자료구조가 제안되었다 각 자료구조의 특성에 따른 알고리즘 성능은 데이터의 패턴에 크게 의존한다. 본 논문에서는 Concept을 형성하는 세가지 대표적인 자료구조인 Hash Tree, Lattice. FP-Tree에 대해 비교 분석해보고, 데이터 패턴에 적합한 효율적인 알고리즘의 설계 위한 framework을 제안한다.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.567-584
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• 2005

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from temporal data. Association rules and classification are applied to various applications which are the typical data mining problems. However, these approaches do not consider temporal attribute and have been pursued for discovering knowledge from static data although a large proportion of data contains temporal dimension. Also, data mining researches from temporal data treat problems for discovering knowledge from data stamped with time point and adding time constraint. Therefore, these do not consider temporal semantics and temporal relationships containing data. This paper suggests that temporal associative classification technique based on temporal class association rules. This temporal classification applies rules discovered by temporal class association rules which extends existing associative classification by containing temporal dimension for generating temporal classification rules. Therefore, this technique can discover more useful knowledge in compared with typical classification techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.133-140
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• 2011

For both customers and companies, it is very important to analyze online customer reviews, which consist of small documents that include opinions or experiences about products or services, because the customers can get good informations and the companies can establish good marketing strategies. In this paper, we propose the association model for the opinion mining which can analyze customer opinions posted on web. The association model is to modify the association rules mining model in data mining in order to apply efficiently and effectively the association mining techniques to the opinion mining. We designed and implemented the opinion mining systems based on the modified association model and the grouping idea which would enable it to generate significant rules more.

• Journal of Digital Contents Society
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• v.17 no.2
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• pp.81-88
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• 2016

The general frequent pattern mining considers frequency and support of items. To extract useful information, it is necessary to consider frequency and weight of items that reflects the changing of user interest as time passes. The suitable services considering time or location is requested by user so that the weighted mining method is necessary. We propose a method of weighted frequent pattern mining based on service ontology. The weight considering time and location is given to service items and it is applied to association rule mining method. The extracted rule is combined with stored service rule and it is based on timely service to offer for user.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.1
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• pp.33-43
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• 2013

The research on data mining that can manage a large amount of information efficiently has grown with the drastic increment of information. Privacy-preserving data mining can protect the privacy of data owners. There are several privacy-preserving association rule, clustering and classification protocols. A privacy-preserving association rule protocol is used to find association rules among data, which is often used for marketing. In this paper, we propose a privacy-preserving k-bits inner product protocol based on Shamir's secret sharing.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.3-6
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• 2001

현 시대에 살아가는 사람들은 정보의 홍수 속에서 살아간다고 해도 과언이 아니다. 컴퓨터 시스템의 발달과 데이터베이스 시스템의 사용의 증가로 컴퓨터에 저장되는 정보의 양은 폭발적으로 증가하고 있다. 현재의 컴퓨터에 저장되어 있는 대용량 데이터베이스에는 사용자가 미처 파악하지 못하는 중요한 정보가 포함되어 있을 수 있다. 본 논문에서는 데이터 상호간의 연관규칙에서 각 항목의 개체수를 고려하여 사용자들에게 좀 더 유용하고 다양한 종류의 데이터를 제공하기 위해 새로운 데이터 마이닝 연관규칙 방법을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.7-9
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• 2001

정보화 시대에 정보의 양이 폭발적으로 증가함에 따라 데이터 마이닝(Data Mining) 또는 데이터베이스에서의 지식 발견이라 불리는 분야가 새로운 정보기술의 활용방법으로 대두되었다. 데이터 마이닝의 한 기법인 연관 규칙 탐사를 위한 자료 구조로 그 동안 해쉬 트리, prefix 트리, 이진 트리 구조 등이 제안되었다. 본 논문에서는 연관 규칙 탐사를 위한 효율적인 자료 구조를 제안하고 실험을 통해 해쉬 트리보다 그 성능이 우수함을 보였다.

• The Journal of Korean Association of Computer Education
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• v.11 no.1
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• pp.65-74
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• 2008

In this paper, we introduce an analysis system for answer dataset by using a data mining method. We analyze students' answer data collected from a test including multiple choice question items, and find associations between the items. Analysis of evaluation results based on our system will not only provide correct information on students' achievement levels but also provides a basis for modifying weaknesses of the evaluation procedures, question items, or teaching/learning procedures. Furthermore, it will enable us to improve the quality of question items for future use so that we can secure itemsets of high quality.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.293-298
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• 2006

Many data are stored in database. For getting any information from many data, we use the query sentences. These information is basic and simple. Data mining method is various. In this paper, we manage clustering and association rules. We present a method for finding the better association rules, and we solve a problem of the existing association rules. We propose and apply a new clustering method to fit for association rules. It is not clustering of the existing distance basis or category basis. If we find association rules of each clusters, we can get not only existing rules found in all transaction but also rules that will be characteristics of clusters. Through this study, we can expect that we will reduce the number of many transaction access in large databases and find association of small group.