• Title/Summary/Keyword: 평가규칙

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The application for predictive similarity measures of binary data in association rule mining (이분형 예측 유사성 측도의 연관성 평가 기준 적용 방안)

  • Park, Hee-Chang
    • 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.

Hybrid Behavior Evolution Model Using Rule and Link Descriptors (규칙 구성자와 연결 구성자를 이용한 혼합형 행동 진화 모델)

  • Park, Sa Joon
    • Journal of Intelligence and Information Systems
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    • v.12 no.3
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    • pp.67-82
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    • 2006
  • We propose the HBEM(Hybrid Behavior Evolution Model) composed of rule classification and evolutionary neural network using rule descriptor and link descriptor for evolutionary behavior of virtual robots. In our model, two levels of the knowledge of behaviors were represented. In the upper level, the representation was improved using rule and link descriptors together. And then in the lower level, behavior knowledge was represented in form of bit string and learned adapting their chromosomes by the genetic operators. A virtual robot was composed by the learned chromosome which had the best fitness. The composed virtual robot perceives the surrounding situations and they were classifying the pattern through rules and processing the result in neural network and behaving. To evaluate our proposed model, we developed HBES(Hybrid Behavior Evolution System) and adapted the problem of gathering food of the virtual robots. In the results of testing our system, the learning time was fewer than the evolution neural network of the condition which was same. And then, to evaluate the effect improving the fitness by the rules we respectively measured the fitness adapted or not about the chromosomes where the learning was completed. In the results of evaluating, if the rules were not adapted the fitness was lowered. It showed that our proposed model was better in the learning performance and more regular than the evolutionary neural network in the behavior evolution of the virtual robots.

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The Descriptive Grade Evaluation System using Fuzzy Decision Making Method (퍼지 의사결정 방법을 이용한 서술식 성적 평가 방법)

  • 김두완;김성국;정환묵
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.05a
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    • pp.213-216
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    • 2003
  • 본 논문에서는 교사가 학생의 성적을 효과적으로 평가하기 위하여 유사 척도 방법을 이용한 서술식 성적평가 시스템을 제안한다. 사용자(교사)로부터 수행평가요소의 결과와 과목의 최종적인 평가를 퍼지 추론에 적용하여 객관적인 성적평가를 한 후, 추론규칙과 실제 학생의 점수의 유사도를 이용하여 가장 높은 값의 성적평가 문장을 추출하여 서술식 평가 문장을 생성하도록 하였다.

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Algorithm mining Association Rules by considering Weight Support (중요지지도를 고려한 연관규칙 탐사 알고리즘)

  • Kim, Keun-Hyung;Whang, Byung-Woong;Kim, Min-Chul
    • The KIPS Transactions:PartD
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    • v.11D no.3
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    • pp.545-552
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    • 2004
  • Association rules mining, which is one of data mining technologies, searches data among which are frequent and related to each other in database. But, although the data are not of frequent and rare in database, they have the enough worth of business information if the data ares important and strongly related to each other, In this paper, we propose the algorithm discovering association rules that consist of data, which are rare but, important and strongly related to each other in database. The proposed algorithm was evaluated through simulation. We found that the proposed algorithm discovered efficiently association rules among data, which are not frequent but, important.

CRG Algorithm and nTCAM for the Efficient Packet Filtering System (효율적인 패킷 필터링 시스템을 위한 CRG 알고리즘과 nTCAM)

  • Kim Yong-Kwon;Lee Soon-Seok;Kim Young-Sun;Ki Jang-Geun
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.8B
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    • pp.745-756
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    • 2006
  • The general packet filtering system using TCAM has some limitations such as range and negation rules filtering, so this paper proposes efficient searching schemes than existing methods. CRG(Converting Range rules using Gray code) algorithm, in the case of range rules, that takes advantage of the gray code and TCAM characteristics to save a number of TCAM entries is proposed, and a nTCAM(TCAM with negation) architecture for negation rules is proposed, implemented using a FPGA design tool, and verified through the wave simulation. According to the simulation with the SNORT rules, the CRG algorithm and nTCAM save TCAM entries about 93% in IPv4 and 98% in IPv6 than the existing method.

Design of Fuzzy Neural Networks Based on Fuzzy Clustering and Its Application (퍼지 클러스터링 기반 퍼지뉴럴네트워크 설계 및 적용)

  • Park, Keon-Jun;Lee, Dong-Yoon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.14 no.1
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    • pp.378-384
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    • 2013
  • In this paper, we propose the fuzzy neural networks based on fuzzy c-means clustering algorithm. Typically, the generation of fuzzy rules have the problem that the number of fuzzy rules exponentially increases when the dimension increases. To solve this problem, the fuzzy rules of the proposed networks are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the learning of fuzzy neural networks is realized by adjusting connections of the neurons, and it follows a back-propagation algorithm. The proposed networks are evaluated through the application to nonlinear process.

A Personalized Clothing Recommender System Based on the Algorithm for Mining Association Rules (연관 규칙 생성 알고리즘 기반의 개인화 의류 추천 시스템)

  • Lee, Chong-Hyeon;Lee, Suk-Hoon;Kim, Jang-Won;Baik, Doo-Kwon
    • Journal of the Korea Society for Simulation
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    • v.19 no.4
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    • pp.59-66
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    • 2010
  • We present a personalized clothing recommender system - one that mines association rules from transaction described in ontologies and infers a recommendation from the rules. The recommender system can forecast frequently changing trends of clothing using the Onto-Apriori algorithm, and it makes appropriate recommendations for each users possible through the inference marked as meta nodes. We simulates the rule generator and the inferential search engine of the system with focus on accuracy and efficiency, and our results validate the system.

자료

  • Korea Institute of Registered Architects
    • Korean Architects
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    • no.10 s.223
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    • pp.85-93
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    • 1987
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