• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.205-208
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• 2007

Measuring the similarity between fuzzy sets plays a vital role in several fields. However, none of all well-known similarity measure methods is all-powerful, and all have the localization of its usage. This paper defines some operations on the similarity measures of fuzzy sets such as summation and multiplication of two similarity measures. Also, these operations will be generalized to any number of similarity measures. These operations will be very useful especially in the field of computer vision, and data retrieval because these fields need to combine and find some relations between similarity measures.

• Korean Management Science Review
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• v.28 no.1
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• pp.61-74
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• 2011

Not only the preference data but also the similarity data can be used for developing effective marketing strategies. Hahn et al.[10] proposes a methodology of representing a brand(focal brand)'s competitors in a single map called the Preference-Similarity Map, according to their relative preference to and similarity with the focal brand. They also proposes a way to derive the relative preference and similarity values from the survey collecting the choice data from differing choice sets. This study identifies the limitations of the preference and similarity measures proposed by Hahn et al.[10] and shows how these measures can be revised. This study also proposes how to implement the revised measures and analyze brands' positioning strategies. Based on the results of the previous studies on the effect of inter brand similarity on brand evaluations, this study assumes that it is important to analyze how much a specific brand is preferred to its close competitors when evaluating the effectiveness of the brand's positioning in the market. This study applies the proposed measures to the data used in Hahn et al.[10] and also show how the proposed measures are related to the parameters of the choice model proposed by Batsell and Polking[1].

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.702-705
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• 2004

Collaborative filtering (CF) is originally based on the ratings of customers who vote on the items they used. When customers' votes are not available, user-item binary data set which represents choice and non-choice can also be used in this analysis. In this case the similarities between active user and the other users must be modified. Therefore we compare eight types of binary similarities by applying them in the modified CF Algorithm. Some experimental results will be reported.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.430-460
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• 2004

This study presented a personalization system that adopted a methodology which is applicable for digital content recommendation and executed by the Internet service providers. The system made a recommendation to the users on the basis of their preferences, while most techniques for recommending digital content have focused on considering the similarity of content. In addition, it developed a method of evaluation to determine the priority of recommendations and adopted measures when selecting a set of recommendations. To experiment the feasibility and effectiveness of the presented methodology, a prototype system was developed and was applied to an English newspaper on the Internet.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.2
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• pp.139-155
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• 2003

The aim of this study is to identify the data traffic capacity of 3G mobile communication networks, especially of cdma2000-1X networks. Three-layered ON/OFF traffic model is used to describe the dynamics of data traffics and the process of data transmission such as packet scheduling. We construct a simulator fully incorporating packet handling process of cdma2000-lX data network as well as three-layered ON/OFF traffic model describing the behavior of source data traffics. To get influence of traffic parameters on performance measures, the extensive simulations were performed for several data sets which are obtained from real trace data or previous studies. The experimental results show that the engineered throughput satisfying QoS criteria is approximately 25% of total capacity. Finally, some proposals to improve the system capacity are followed.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.1-16
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• 2008

The purpose of this paper is to propose a new binary classification method for predicting corporate failure based on genetic algorithm, and to validate its prediction power through empirical analysis. Establishing virtual companies representing bankrupt companies and non-bankrupt ones respectively, the proposed method measures the similarity between the virtual companies and the subject for prediction, and classifies the subject into either bankrupt or non-bankrupt one. The values of the classification variables of the virtual companies and the weights of the variables are determined by the proper model to maximize the hit ratio of training data set using genetic algorithm. In order to test the validity of the proposed method, we compare its prediction accuracy with ones of other existing methods such as multi-discriminant analysis, logistic regression, decision tree, and artificial neural network, and it is shown that the binary classification method we propose in this paper can serve as a premising alternative to the existing methods for bankruptcy prediction.