• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.395-399
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• 2003

본 논문에서는 인문 사회과학 분야의 방대한 설문 데이터를 처리하기 위해 기존의 설문 항목들간의 평면적 관계에만 국한 되었던 연구에 대해 설문데이터 다차원 연관규칙 마이닝 시스템을 설계하고 데이터 간의 연관규칙을 탐사한다. 즉, 직관적으로 분류될 수 있는 기준에 따라 클러스터링을 실행하여 데이터를 분류한 후 각 클러스터로부터 다차원 연관 규칙을 탐사하는 시스템을 제안함으로써 보다 강력한 연관규칙을 탐사한다.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1113-1121
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• 2011

Recently, a variety of data mining techniques has been applied in various fields like healthcare, insurance, and internet shopping mall. Association rule mining is a popular and well researched method for discovering interesting relations among large set of data items. Association rule mining is the method to quantify the relationship between each set of items in very huge database based on the association thresholds. There are three primary quality measures for association rules; support and confidence and lift. In this paper we consider some similarity measures with negative co-occurrence frequencies which is widely used in cluster analysis or multi-dimensional analysis as association thresholds. The comparative studies with support, confidence and some similarity measures are shown by numerical example.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.495-503
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• 2011

The most widely used data mining technique is to find association rules. Association rule mining is the method to quantify the relationship between each set of items in very huge database based on the association thresholds. There are some basic association thresholds to explore meaningful association rules ; support, confidence, lift, etc. Among them, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The net confidence and the attributably pure confidence were developed to compensate for this drawback, but they have other drawbacks.In this paper we consider some predictive similarity measures for binary data in cluster analysis and multi-dimensional analysis as association threshold to compensate for these drawbacks. The comparative studies with net confidence, attributably pure confidence, and some predictive similarity measures are shown by numerical example.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.233-236
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• 2001

멀티미디어 데이터의 증가와 마이닝 기술의 발전으로 인해 멀티미디어 마이닝에 대한 관심이 증가하고 있다. 본 논문에서는 특성국지화를 이용한 내용기반의 정보검색 기술과 다차원 데이터큐브 구축기술을 통해 멀티미디어 데이터에서 연관규칙을 찾아내는 멀티미디어 데이터마이닝 시스템 프로토타입을 제안한다. 특히 멀티미디어 데이터의 칼라, 질감 등 거시적인 이미지 성분 대신 이미지의 영역성과 유사성을 이용한 특성국지화방법을 이용하여 이미지를 분할함으로써 방대한 데이타에서 효과적인 내용기반의 정의 검색을 시행하고 검색한 벡터를 메타데이타로 한 데이스베이스를 구축한다. 그리고 데이터베이스에서 데이터간 연관규칙을 찾아내어 지식을 마이닝하는데 효과적인 다차원 데이터큐브를 구축하고 여기에 연관규칙 검색 알고리즘을 적용한다.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.565-573
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• 2017

The ex-post evaluation of governmental crisis management is an important issues since it is necessary to prepare for the future disasters and becomes the cornerstone of our success as well. In this paper, we propose a data cube model with data mining techniques for the analysis of governmental crisis management strategies and ripple effects of foot-and-mouth(FMD) disease using the online news articles. Based on the construction of the data cube model, a multidimensional FMD analysis is performed using on line analytical processing operations (OLAP) to assess the temporal perspectives of the spread of the disease with varying levels of abstraction. Furthermore, the proposed analysis model provides useful information that generates the causal relationship between crisis response actions and its social ripple effects of FMD outbreaks by applying association rule mining. We confirmed the feasibility and applicability of the proposed FMD analysis model by implementing and applying an analysis system to FMD outbreaks from July 2010 to December 2011 in South Korea.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.60-64
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• 2010

To discover association rules from nontransactional data, there have been many studies on discretization of attribute values. These studies do not reflect the change of discovered rules' confidence according to the change of the ranges of the discretized attributes, and perform the discretization stage and the rule discovery stage independently. This causes the ranges of attributes not properly discretized, thereby making the rules having high confidence excluded in the result set. To solve this problem, we propose a novel method that performs the discretization and rule discovery stages simultaneously in order to discretize ranges of attributes in such a way that the rules having high confidence are discovered well. To the end, we perform hierarchical clustering on the attributes in the right hand side of rules, then do characterization on every cluster thus obtained. The experimental result demonstrates that our method discovers the rules having high confidence better than existing methods.