• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1061-1069
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• 2010

In data mining, association rule is a popular and well researched method for discovering interesting relations between variables. There are three measures for association rule, support, confidence and lift. But there are some problem in them. They don't consider the frequency of variable in case. So, we need the new association rule which consider the frequency.In this paper, we proposed the new association rule. We compared the proposed association rule with the original association rule from example data. As a result, we knew our function was better than the original function in terms of sensitivity.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.218-219
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• 2011

This paper focuses on dispatching strategy for AGVs(Automated Guided Vehicle). The goal of AGV dispatching problem is allocating jobs to AGVs to minimizing QC delay and AGV total travel distance. Due to the highly dynamic nature of container terminal environment, the effect of dispatching is hard to predict thus it leads to frequent modification of dispatching results. Given this situation, single rule-based approach is widely used due to its simplicity and small computational cost. However, single rule-based approach has a limitation that cannot guarantee a satisfactory performance for the various performance measures. In this paper, dispatching strategy based on multicriteria heuristics is proposed. Proposed strategy consists of multiple decision criteria. A muti-objective evolutionary algorithm is applied to optimize weights of those criteria. The result of simulation experiment shows that the proposed approach outperforms single rule-based dispatching approaches.

• Journal of Navigation and Port Research
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• v.35 no.6
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• pp.501-507
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• 2011

This paper focuses on dispatching strategy for AGVs(Automated Guided Vehicle). The goal of AGV dispatching is assigning AGVs to requested job to minimizing the delay of QCs and the travel distance of AGVs. Due to the high dynamic nature of container terminal environment, the effect of dispatching is hard to predict thus it leads to frequent modification of dispatching decisions. In this situation, approaches based on a single rule are widely used due to its simplicity and small computational cost. However, these approaches have a limitation that cannot guarantee a satisfactory performance for the various performance measures. In this paper, dispatching strategy based on multicriteria heuristics is proposed. The Proposed strategy consists of multiple decision criteria. A multi-objective evolutionary algorithm is applied to optimize weights of those criteria. The result of simulation experiment shows that the proposed approach outperforms single rule-based dispatching approaches.

• Korean Journal of Logic
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• v.19 no.1
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• pp.49-82
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• 2016

In this paper, I argue that it is Kripke's Tractarian notion of rule-following that prevents him from giving a non-skeptical (straight) solution to Wittgenstein's paradox. I characterize the Tractarian notion of rule-following as the 'determinate/infinistic' notion of rule-following. The later Wittgenstein, however, advocates an opposite notion of rule-following: the 'indeterminate/finistic' notion. Considering the later Wittgenstein's context-sensitive, pragmatics-oriented approach to meaning and rule-following, the later Wittgenstein could not have endorsed the determinate/infinistic notion of rule-following. To the contrary, a motive behind Wittgenstein's skeptical paradox was to blame the Tractarian notion of rule-following as the major culprit giving rise to the paradox. At the end, I argue that Kripke's adherence to the Tractarian-correspondence theory of truth also contributes to his failure to offer a non-skeptical solution to the paradox. If Kripke had noticed that the later Wittgenstein was a deflationist about truth, he could have avoided his skeptical conclusion.

• Journal of Korean Society of societal Security
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• v.4 no.2
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• pp.29-40
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• 2011

The objective of this paper is to develop an intelligent decision support system that is able to advise disaster countermeasures and degree of incidents on the basis of the collected and analyzed signs of disasters. The concepts derived from ontology, text mining and case-based reasoning are adapted to design the system. The functions of this system include term-document matrix, frequency normalization, confidency, association rules, and criteria for judgment. The collected qualitative data from signs of new incidents are processed by those functions and are finally compared and reasoned to past similar disaster cases. The system provides the varying degrees of how dangerous the new signs of disasters are and the few countermeasures to the disaster for the manager of disaster management. The system will be helpful for the decision-maker to make a judgment about how much dangerous the signs of disaster are and to carry out specific kinds of countermeasures on the disaster in advance. As a result, the disaster will be prevented.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.161-165
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• 2014

In this paper, we proposed the fuzzy prototype pattern classifier. In the proposed classifier, each prototype is defined to describe the related sub-space and the weight value is assigned to the prototype. The weight value assigned to the prototype leads to the change of the boundary surface. In order to define the prototypes, we use Fuzzy C-Means Clustering which is the one of fuzzy clustering methods. In order to optimize the weight values assigned to the prototypes, we use the Differential Evolutionary Algorithm. We use Linear Discriminant Analysis to estimate the coefficients of the polynomial which is the structure of the consequent part of a fuzzy rule. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.553-560
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• 2001

Bioinformatics is a discipline to support biological experiment projects by storing, managing data arising from genome research. In can also lead the experimental design for genome function prediction and regulation. Among various approaches of the genome research, the proteomics have been drawing increasing attention since it deals with the final product of genomes, i.e., proteins, directly. This paper proposes a data mining technique to predict the structural characteristics of a given protein group, one of dominant factors of the functions of them. After explains associations among amino acid subsequences in the primary structures of proteins, which can provide important clues for determining secondary or tertiary structures of them, it defines a sequence association rule to represent the inter-subsequences. It also provides support and confidence measures, newly designed to evaluate the usefulness of sequence association rules, After is proposes a method to discover useful sequence association rules from a given protein group, it evaluates the performance of the proposed method with protein sequence data from the SWISS-PROT protein database.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.360-366
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• 2008

The initial fuzzy partitions in fuzzy rule-based classification systems are determined by considering the domain region of each attribute with the given data, and the optimal classification boundaries within the fuzzy partitions can be discovered by tuning their parameters using various learning processes such as neural network, genetic algorithm, and so on. In this paper, we propose a selection method for fuzzy partition based on statistical information to maximize the performance of pattern classification without learning processes where statistical information is used to extract the uncertainty regions (i.e., the regions which the classification boundaries in pattern classification problems are determined) in each input attribute from the numerical data. Moreover the methods for extracting the candidate rules which are associated with the partition intervals generated by statistical information and for minimizing the coupling problem between the candidate rules are additionally discussed. In order to show the effectiveness of the proposed method, we compared the classification accuracy of the proposed with those of conventional methods on the IRIS and New Thyroid Cancer data. From experimental results, we can confirm the fact that the proposed method only considering statistical information of the numerical patterns provides equal to or better classification accuracy than that of the conventional methods.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.291-303
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• 2006

Data mining is the work to extract information from existing data file. So, the one of best important thing in data mining process is the quality of data to be used. In this thesis, we propose the data fusion technique using decision rule for data enrichment that one phase to improve data quality in KDD process. Simulations were performed to compare the proposed data fusion technique with the existing techniques. As a result, our data fusion technique using decision rule is characterized with low MSE or misclassification rate in fusion variables.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.291-300
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• 1999

본 논문은 인공신경망과 귀납적 학습방법 등의 인공지능 방법과 선물가격결정에 대한 기존 재무이론을 사용하여 일상어휘로 표현되는 파생상품 가격예측 모형을 개발하는데 있다. 모형의 개발은 1단계로 인공신경망이나 기존의 선물가격결정이론(평균보유비용모형이나 일반균형모형)을 이용하여 선물 가격을 예측한 후, 서로 비교분석하여 인공신경망 모형의 우수성을 확인하였다. 귀납적 학습방법중 CART 알고리듬을 사용하여 If-Then 규칙을 생성하였다. 특히 실용적 측면에서 선물가격의 일상어휘화를 통한 모형개발을 여러 가지 방법으로 시도하였다. 이러한 선물가격 예측모형의 유용성은 일단 If-Then 규칙으로 표현되어 전문가의 판단에 확실한 이론적인 근거를 제시할 수 있는 장점이 있으며, 특히 의사결정지원시스템으로 활용화 될 경우 매우 유용한 근거자료로 활용될 수 있다. 이러한 선물가격 예측모형은 정확성은 분석표본과 검증표본으로 나누어 검증표본에서 세가지 기본모형(평균보유비용모형, 일반균형모형, 인공신경망 모형)과 각 모형의 귀납적 학습방법 모형의 다른 3가지 어휘표현방법 3가지를 모형별로 비교 분석하였다. 분석결과 인공신경망모형은 상당한 예측력을 갖고 있는 것으로 판명되었으며, 특히 CART를 기반으로 한 일상어휘 기반의 선물가격예측 모형은 예측력이 높은 것으로 나타났다.