• Title/Summary/Keyword: association rule learning

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Extraction of Expert Knowledge Based on Hybrid Data Mining Mechanism (하이브리드 데이터마이닝 메커니즘에 기반한 전문가 지식 추출)

  • Kim, Jin-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.6
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    • pp.764-770
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    • 2004
  • This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

Comparison of Association Rule Learning and Subgroup Discovery for Mining Traffic Accident Data (교통사고 데이터의 마이닝을 위한 연관규칙 학습기법과 서브그룹 발견기법의 비교)

  • Kim, Jeongmin;Ryu, Kwang Ryel
    • Journal of Intelligence and Information Systems
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    • v.21 no.4
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    • pp.1-16
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    • 2015
  • Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

Generalized Fuzzy Quantitative Association Rules Mining with Fuzzy Generalization Hierarchies

  • Lee, Keon-Myung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.2 no.3
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    • pp.210-214
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    • 2002
  • Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

Multi-layer Neural Network with Hybrid Learning Rules for Improved Robust Capability (Robustness를 형성시키기 위한 Hybrid 학습법칙을 갖는 다층구조 신경회로망)

  • 정동규;이수영
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.8
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    • pp.211-218
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    • 1994
  • In this paper we develope a hybrid learning rule to improve the robustness of multi-layer Perceptions. In most neural networks the activation of a neuron is deternined by a nonlinear transformation of the weighted sum of inputs to the neurons. Investigating the behaviour of activations of hidden layer neurons a new learning algorithm is developed for improved robustness for multi-layer Perceptrons. Unlike other methods which reduce the network complexity by putting restrictions on synaptic weights our method based on error-backpropagation increases the complexity of the underlying proplem by imposing it saturation requirement on hidden layer neurons. We also found that the additional gradient-descent term for the requirement corresponds to the Hebbian rule and our algorithm incorporates the Hebbian learning rule into the error back-propagation rule. Computer simulation demonstrates fast learning convergence as well as improved robustness for classification and hetero-association of patterns.

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A Study on the Hybrid Data Mining Mechanism Based on Association Rules and Fuzzy Neural Networks (연관규칙과 퍼지 인공신경망에 기반한 하이브리드 데이터마이닝 메커니즘에 관한 연구)

  • Kim Jin Sung
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2003.05a
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    • pp.884-888
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    • 2003
  • In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

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An Adaptive Evaluation System Using Fuzzy Reasoning Rule (퍼지추론규칙을 이용한 적응형 평가시스템)

  • Um, Myoung-Yong;Jung, Soon-Young;Lee, Won-Gyu
    • The Journal of Korean Association of Computer Education
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    • v.6 no.4
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    • pp.95-113
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    • 2003
  • We introduce an AFES(Adaptive Fuzzy Evaluation System) that applies an evaluation system used to existing LCMS(Learning Contents Management System) to a fuzzy reasoning rule. The AFES confers a course level on the learner through a fuzzy diagnostic evaluation before the learner enters a learning course. After the learner completes a learning course through the tailored learning path that is suitable for the learner's level, the AFES confers a final grade on the learner by means of fuzzy final evaluation. The biggest characteristic of the AFES is a grade rule of the final grade. Although different learners get the same number of correct answers to the same number of Questions, AFES flexibly confers the different final grade on the learner by means of the number of 125's fuzzy reasoning rules.

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Development of association rule threshold by balancing of relative rule accuracy (상대적 규칙 정확도의 균형화에 의한 연관성 측도의 개발)

  • Park, Hee Chang
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.6
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    • pp.1345-1352
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    • 2014
  • Data mining is the representative methodology to obtain meaningful information in the era of big data.By Wikipedia, association rule learning is a popular and well researched method for discovering interesting relationship between itemsets in large databases using association thresholds. It is intended to identify strong rules discovered in databases using different interestingness measures. Unlike general association rule, inverse association rule mining finds the rules that a special item does not occur if an item does not occur. If two types of association rule can be simultaneously considered, we can obtain the marketing information for some related products as well as the information of specific product marketing. In this paper, we propose a balanced attributable relative accuracy applicable to these association rule techniques, and then check the three conditions of interestingness measures by Piatetsky-Shapiro (1991). The comparative studies with rule accuracy, relative accuracy, attributable relative accuracy, and balanced attributable relative accuracy are shown by numerical example. The results show that balanced attributable relative accuracy is better than any other accuracy measures.

신경망기법을 이용한 기업부실예측에 관한 연구

  • Jeong, Gi-Ung;Hong, Gwan-Su
    • The Korean Journal of Financial Management
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    • v.12 no.2
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    • pp.1-23
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    • 1995
  • 본 연구의 목적은 특정 금융기관의 주거래기업들에 대한 부실예측을 위해 주거래기업들을 잠식, 도산, 그리고 건전기업과 같이 세집단으로 구분하여 예측하고자 하며, 기업부실 예측력에 영향을 미치는 세 가지 요인으로서 표본구성, 투입 변수, 분석 기법의 관점에서 다음을 살펴보는 것이다. 첫째, 기업부실예측에서 전통적인 delta learning rule과 sigmoid함수를 사용한 역전파학습(신경망 I)과 이들의 변형형태인 normalized cumulative delta learning rule과 hyperbolic tangent함수를 사용한 역전파 학습(신경망 II)과의 예측력의 차이를 살펴보고 또한 이러한 두가지 신경망기법의 예측력을 MDA(다변량판별분석) 결과와 비교하여 신경망기법에 대한 예측력의 유용성을 살펴보고자 한다. 둘째, 세집단분류문제에서는 잠식, 도산, 건전기업의 구성비율이 위의 세가지 예측기법의 결과에 어떠한 영향을 미치는지를 살펴보고자 한다. 세째, 투입 변수선정은 기존연구 또는 이론을 바탕으로 연구자의 판단에 의해 선택하는 방법과 다수의 변수를 가지고 통계적기법에 의해 좋은 판별변수의 집합을 찾는 것이다. 본 연구에서는 이러한 방법들에 의해 선정된 투입변수들이 세가지 예측기법의 결과에 어떠한 영향을 미치는지를 살펴보고자 한다. 이러한 관점에서 본 연구의 실증분석 결과를 요약하면 다음과 같다. 1) 신경망기법이 두집단에서와 같이 세집단 분류문제에서도 MDA보다는 더 높은 예측력을 보였다. 2) 잠식과 도산기업의 수는 비슷하게 그리고 건전기업의 수는 잠식과 도산기업을 합한 수와 비슷하게 표본을 구성하는 것이 예측력을 향상하는데 도움이 된다고 할 수 있다. 3) 속성별로 고르게 투입변수로 선정한 경우가 그렇지 않은 경우보다 더 높은 예측력을 보였다. 4) 전통적인 delta learning rule과 sigmoid함수를 사용한 역전파학습 보다는 normalized cumulative delta learning rule과 hyperbolic tangent함수를 사용한 역전파 학습이 더 높은 예측력을 보였다. 이러한 현상은 두집단문제에서 보다 세집단문제에서 더 큰 차이를 나타내고 있다.

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A Rule-driven Automatic Learner Grouping System Supporting Various Class Types (다양한 수업 유형을 지원하는 규칙 기반 학습자 자동 그룹핑 시스템)

  • Kim, Eun-Hee;Park, Jong-Hyun;Kang, Ji-Hoon
    • Journal of The Korean Association of Information Education
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    • v.14 no.3
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    • pp.291-300
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    • 2010
  • Group-based learning is known to be an effective means to improve scholastic achievement in online learning. Therefore, there are some previous researches for the group-based learning. A lot of previous researches define factors for grouping from the characteristics of classes, teacher's decision and students' preferences and then generate a group based on the defined factors. However, many algorithms proposed by previous researches depend on a specific class and is not a general approach since there exist several differences in terms of the need of courses, learners, and teachers. Moreover it is hard to find a automatic system for group generation. This paper proposes a grouping system which automatically generate a learner group according to characteristics of various classes. the proposed system automatically generates a learner group by using basic information for a class or additional factors inputted from a user. The proposed system defines a set of rules for learner grouping which enables automatic selection of a learner grouping algorithm tailored to the characteristics of a given class. This rule based approach allows the proposed system to accommodate various learner grouping algorithms for a later use. Also we show the usability of our system by serviceability evaluation.

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A study of Intrusion Detection System applying for association rule agent (연관규칙 에이전트를 적용한 침입 탐지 시스템에 관한 연구)

  • 박찬호;정종근
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.5
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    • pp.684-688
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    • 2002
  • One of the Problems, which the Intrusion Detection System has, is a False Positive. This False make to low condition of the Intrusion Detection System. The cause of the False Positive is that the learning is not enough during audit data learning steps. Therefore, in this paper, 1 propose the method of the Intrusion Detection System that be learnt audit data to agent with association rule.