• Journal of the Korea Computer Industry Society
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• v.2 no.11
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• pp.1365-1372
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• 2001

We have suggested three learning rules and implemented an experimental system that automatically restructures hypertext networks according to their user browsing behavior. The system applies link weights to the hypertext networks and updates these weights according to the learning rules. The learning rules are based on how often a particular hyperlink is being traversed and operate on strictly local information of link traversals. Changes in network structure are fed back to users by dynamic link ordering according to descending link weight. This approach is used collaborative filtering and navigation support techniques. The system has been investigation to be able to structure random hypertext networks into valid representations of their user browsing preferences in experiments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.451-454
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• 2002

본 논문에서는 개인의 적성을 판단하는 문제를 처리하기 위한 가중치 퍼지추론 알고리즘을 제시하고, 지식표현을 위해 퍼지 집합 이론과 퍼지 생성 규칙들을 이용하였다. 거리척도에 서는 퍼지값이 높은 구간의 척도를 낮은 구간의 척도에 비례하여 유사성을 구하였다. 또한, 가중치를 정량화한 값과 척도값을 연산하여 유사성을 나타냈고, 추출된 항목과 규칙과의 가능성을 구하였다. 여기서, 결과는 수검자들이 응답한 값들에 따라 임의의 직업군이 적당한 지를 나타내기 위해 확신도로 해석하였다.

• 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.

• Journal of Digital Contents Society
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• v.17 no.2
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• pp.81-88
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• 2016

The general frequent pattern mining considers frequency and support of items. To extract useful information, it is necessary to consider frequency and weight of items that reflects the changing of user interest as time passes. The suitable services considering time or location is requested by user so that the weighted mining method is necessary. We propose a method of weighted frequent pattern mining based on service ontology. The weight considering time and location is given to service items and it is applied to association rule mining method. The extracted rule is combined with stored service rule and it is based on timely service to offer for user.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.11-15
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• 2006

본 논문은, 긍정적 가중치와 부정적 가중치를 통해 표현되는 규칙에 기반을 둔 품사 태깅 모델과, 형태 소 unigram 정보와 어절 내의 카테고리 패턴에 기반하여 어절 확률을 추정하는 품사 태깅 모델의 장점을 취하고 단점을 보완할 수 있는 혼합 품사 태깅 모델을 제안한다. 이 혼합 모델은 먼저, 규칙에 기반한 품사 태깅을 적용한 후, 규칙이 해결하지 못한 결과에 대해서 통계적인 기법을 사용하여 품사 태깅을 한다. 본 연구는 어절 내 카테고리 패턴정보에 따른 파라미터 set과 형태소 unigram만을 이용해 어절 확률을 계산해 내므로 다른 통계기반 접근방법에서와는 달리 작은 크기의 통계사전만을 필요로 하며, 카테고리 패턴 정보를 사용함으로써 통계기반 접근 방법의 가장 큰 문제점인 data sparseness 문제 또한 줄일 수 있다는 이점이 있다. 특히, 본 논문에서 사용할 통계 모델은 어절 확률에 기반을 두고 있기 때문에 한국어의 특성을 잘 반영할 수 있다. 본 논문에서 제안한 혼합 모델은 규칙이 적용된 후에도 후보열이 둘 이상 남아 오류로 반환되었던 어절 중 24%를 개선한다.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.3
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• pp.145-154
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• 2016

Because of the recent interest in the u-Health and development of IT technology, a need of utilizing a medical information data has been increased. Among previous studies that utilize various data mining algorithms for processing medical information data, there are studies of association rule analysis. In the studies, an association between the symptoms with specified diseases is the target to discover, however, infrequent terms which can be important information for a disease diagnosis are not considered in most cases. In this paper, we proposed a new association rule mining system considering the importance of each term using TF-IDF weight to consider infrequent but important items. In addition, the proposed system can predict candidate diagnoses from medical text records using term similarity analysis based on medical ontology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1284-1290
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• 2004

In market-basket analysis, weighted association rule(WAR) discovery can mine the rules which include more beneficial information by reflecting item importance for special products. However, when items are divided into more than one group and item importance for each group must be measured by different measurement or separately, we cannot directly apply traditional weighted association rule discovery. To solve this problem, we propose a novel methodology to discovery the weighted association rule in this paper In this methodology, the items should be first divided into sub-groups according to the properties of the items, and the item importance is defined or calculated only with the items enclosed to the sub-group. Our algorithm makes qualitative evaluation for network risk assessment possible by generating risk rule set for risk factor using network sorority data, and quantitative evaluation possible by calculating risk value using statistical factors such as weight applied in rule generation. And, It can be widely used for new model of more delicate analysis in market-basket database in which the data items are distinctly separated.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.45-52
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• 2005

We proposed the product recommendation algorithm mixed the temporal association rule and the exponential smoothing method. The temporal association rule added a temporal concept in a commercial association rule In this paper. we proposed a exponential smoothing temporal association rule that is giving higher weights to recent data than past data. Through simulation and case study in temporal data sets, we confirmed that it is more Precise than existing temporal association rules but consumes running time.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1347-1350
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• 2003

최근에 들어서 Web Pgae 및 서버에 악의적인 사용자들로 하여금 많은 피해가 발생하고 있다. 본 논문에서는 연관규칙을 이용한 침입탐지 시스템을 구현함으로써 해킹 및 부정사용자를 방지하여 시스템의 가용성, 효율성을 높이고 안정적인 운용을 제공한다. 그리고 연관규칙의 신뢰성을 높이기 위하여 가중치 개념을 사용하여 효율적인 침입탐지 시스템 구현을 제시하였다.

• Journal of the Society of Disaster Information
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• v.7 no.3
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• pp.220-228
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• 2011

Disaster sign data is confirmed data by the experts to the collected data from web and users. In this paper, we focused to make the risk scores to the data based on ontology technology. To analyse the data, first of all, we defined the ontological structure for 4 kinds of disaster types which consists of the bridges, workplaces, buildings, and walls. Base on the ontologies, collected the accidents examples, and then extract the risk rules from the examples. The rules are adjusted with frequencies and weights, and managed to the ontology DB. The rules apply to the disaster sign data, and then calculates the risk scores. It plays role of the index to the risk rates. The disaster sign data management system was implemented and the rules were verified to the system. Because the quality of the risk scores to the disaster sign data depends on the data of the accidents examples's qualities, we assure that the system's performance will be monotonic increasing following up the data upgrades. Continuously, data management is needed. Also the quality control of the rules are needed.