• 전기학회논문지
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• 제57권11호
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• pp.1916-1921
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• 2008

This paper presents the temporal classification method based on data mining techniques for discovering knowledge from measured load patterns of distribution transformers. Since the power load patterns have time-varying characteristics and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Therefore, we propose a temporal classification rule for analyzing and forecasting transformer load patterns. The main tasks include the load pattern mining framework and the calendar-based expression using temporal association rule and 3-dimensional cube mining to discover load patterns in multiple time granularities.

• Journal of the Korean Data and Information Science Society
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• 제22권1호
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• pp.81-88
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• 2011

연관성규칙은 대용량 데이터베이스에서 각 항목들 간의 관련성을 찾아내는 기법으로 둘 또는 그 이상의 품목들 사이의 지지도, 신뢰도, 향상도를 바탕으로 관련성 여부를 측정한다. 연관성규칙에서는 일반적으로 사용하는 연관성규칙 이외에 연관성규칙의 효율성을 개선하기 위하여 여러 가지 제약기반 연관성규칙의 연구가 활발하게 진행되고 있다. 연관성규칙 생성 시, 종종 많은 규칙들을 발견할 수 있다. 이는 변수들 간에 우연히 관련성이 높게 나타나는 경우가 존재할 수 있고 매개변수에 의하여 직접적인 관련성이 없는 규칙을 발견할 수도 있다. 이에 본 논문에서는 연관성규칙에서 매개변수에 의한 의미 없는 규칙의 발견에 관하여 연구하고자 한다. 본 연구 결과는 연관성 규칙에서 생성된 규칙에 대한 관련성을 보다 정확하게 이해할 수 있도록 함으로써 결과의 해석을 보다 명확하게 할 수 있다.

• 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.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2006년도 PROCEEDINGS OF JOINT CONFERENCEOF KDISS AND KDAS
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• pp.241-251
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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, 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 analysis outputs for environmental preservation and environmental improvement.

• 사물인터넷융복합논문지
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• 제9권1호
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• pp.77-87
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• 2023

네트워크 침입 탐지 작업에 다양한 연관 규칙 마이닝 알고리즘을 적용하는 데에는 두 가지 중요한 문제가 있다. 생성된 규칙 집합의 크기가 너무 커서 IoT 시스템에서 활용하기 어렵고, 거짓 부정/긍정 비율을 제어하기 어렵다. 본 연구에서는 coverage와 exclusion이라는 새로 정의된 척도에 기반을 둔 연관 규칙 마이닝 알고리즘을 제안한다. Coverage는 한 클래스의 트랜잭션에서 패턴이 발견되는 빈도를 나타내고, exclusion은 다른 클래스의 트랜잭션에서 패턴이 발견되지 않는 빈도를 나타낸다. 우리는 KDDcup99라는 공개 데이터 세트를 사용하여 가장 유명한 알고리즘인 Apriori 알고리즘과 실험적으로 제안된 알고리즘을 비교한다. Apriori와 비교하여 제안된 알고리즘은 정확도를 완전히 유지하면서 생성되는 규칙 집합 크기를 최대 93.2%까지 줄인다. 또한, 제안된 알고리즘은 생성된 규칙의 거짓 부정/긍정 비율을 매개변수별로 완벽하게 제어한다. 따라서 네트워크 분석가는 두 가지 문제를 해결함으로써 제안한 연관 규칙 마이닝을 네트워크 침입 탐지 작업에 효과적으로 적용할 수 있다.

• 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.

• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.925-936
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• 2008

An association rule mining finds the relation among each items in massive volume database. In generating association rules, the researcher specifies the measurements randomly such as support, confidence and lift, and produces the rules. The rule is not produced if it is not suitable to the one any condition which is given value. For example, in case of a little small one than the value which a confidence value is specified but a support and lift's value is very high, this rule is meaningful rule. But association rule mining can not produce the meaningful rules in this case because it is not suitable to a given condition. Consequently, we creat insignificant error which is not selected to the meaningful rules. In this paper, we suggest clustering technique to association rule measures for finding effective association rules using measure of association.

• Journal of the Korean Data and Information Science Society
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• 제17권4호
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• pp.1077-1083
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• 2006

Association rule mining searches for interesting relationships among items in a given database. 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. In this paper we present association rule mining based antecedent variables. We call these rules to antecedent association rules. An antecedent variable is a variable that occurs before the independent variable and the dependent variable.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 추계학술대회
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• pp.177-188
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• 2005

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 threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.149-160
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• 2006

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 threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.