• 정보처리학회논문지A
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• 제12A권5호
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• pp.385-390
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• 2005

`집합 제한 논리 언어`는 `집합 이론`을 프로그래밍에 도입한 언어이다. 본 논문은 A. Dovier 연구팀이 제안한 집합 제한 문제 풀이(solver) 절차를 소개하고, 이 절차가 논리 언어 Prolog 상에서 어떻게 구현 가능한 지를 보인다. 이 절차는 `다시쓰기 규칙(rewrite rule)`으로 표현되어 있는데 이 표현의 특징은 일반 프로그래밍 언어가 표현하기 힘든 비결정적 규칙 적용(nondeterministic rule application)과 수학적 변수 (mathematical variable)를 사용한다는 점이다. 본 연구에서는 이들 특징이 Prolog 언어에서 제공되는 비결정적 제어 (nondeterministic control), 논리적 변수(logical variable) 및 리스트(list) 자료구조의 사용으로 쉽게 구현 가능함을 보인다. 본 연구의 구현은 다음과 같은 의의를 가지고 있다. 첫째 본연구는 이 언어의 모든 기능을 완전하게 구현하였다는 점이다. 둘째 본 연구에서는 이 언어의 구현 방법을 누구나 알기 쉽게 기술하였다는 점이다. 셋째 기존의 구현이 상업적 Prolog인 SICStus Prolog를 사용하여 구현한 것과는 달리 본 구현은GNU GPL(General Public License)을 가지는CIAO Prolog를 사용하여 구현하였기 때문에 누구나 자유롭게 사용할 수 있는 점이다. 넷째 본 연구에서 개발된 소스 코드는 공개 소프트웨어이기 때문에 누구나 자유롭게 사용, 수정 및 배포할 수 있다는 점이다.

• 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.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.529-538
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• 2005

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 association rules, decision tree, clustering, neural network and so on. Association rule mining searches for interesting relationships among items in a riven 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 quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1991년도 추계학술대회 논문집
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• pp.121-125
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• 1991

In order to minimize fixing error of workpieces, prismatic and cylindrical types. Modification Rule by Indexing Table and Modification Rule by NC Program are developed for machining centers by using touch trigger probes. The Modification Rule by Indexing Table means the alignment of workpiece to NC program through degree of freedoms of indexing table. The Modification Rule by NC Program is the alignment of NC program to workpiece set-up condition via the generation of NC program. A postprocessing module is also developed for generating NC-part Program (User Macro) to compensate for Machining errors in end milling and boring processes. Developed methods are verified by experiments.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2004년도 추계학술대회
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• pp.115-124
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• 2004

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 association rules, decision tree, clustering, neural network and so on. 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 quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.

• 산업공학
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• 제11권1호
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• pp.119-129
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• 1998

The relative importance of Logistics in steelworks industry is rather higher among other business activities. The objective of the paper is to propose the methodology for real-time vehicle scheduling for scrap disposal in the steelworks industry. Currently, the rule necessary to assign vehicles to a specific job is strictly fixed. The paper adopts the multi-pass rule selector (MPRS) that suggests a promising rule used for vehicle dispatching for a period of time. The MPRS is regularly invoked if necessary and then evaluates a set of rule candidates to select the best rule with respect to the system performance criteria. The experiment shows that the proposed approach outperforms the current single-pass strategy.

• 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.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.1254-1263
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• 1993

Representing knowledge uncertainty , aggregating evidence confidences , and propagation uncertainties are three key elements that effect the ability of a rule-based expert system to represent domains with uncertainty . Fuzzy set theory provide a good mathematical tool for representing the vagueness associated with a variable when , as the condition of a rule , it only partially corresponds to the input data. However, the aggregation of ANDed and Ored confidences is not as simple as the intersection and union operators defined for fuzzy set membership. There is, in fact, a certain degree of compensation that occurs when an expert aggregates confidences associated with compound evidence . Further, expert often consider individual evidences to be varying importance , or weight , in their support for a conclusion. This paper presents a flexible approach for evaluating evidence and conclusion confidences. Evidences may be represented as fuzzy or nonfuzzy variables with as associat d degree of certainty . different weight can also be associated degree of certainty. Different weights can also be assigned to the individual condition in determining the confidence of compound evidence . Conclusion confidence is calculated using a modified approach combining the evidence confidence and a rule strength. The techniques developed offer a flexible framework for representing knowledge and propagating uncertainties. This framework has the potention to reflect human aggregation of uncertain information more accurately than simple minimum and maximum operator do.