• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.200-205
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• 2008

For data analysis, fuzzy entropy is introduced as the measure of fuzziness, similarity measure is also constructed to represent similarity between data. Similarity measure between fuzzy membership functions is constructed through distance measure, and the proposed similarity measure are proved. Application of proposed similarity measure to the example of reliable data selection is also carried out. Application results are compared with the previous results that is obtained through fuzzy entropy and statistical knowledge.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.67-78
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• 2018

This article proposes the modified KNN (K Nearest Neighbor) algorithm which considers the feature similarity and is applied to the word categorization. The texts which are given as features for encoding words into numerical vectors are semantic related entities, rather than independent ones, and the synergy effect between the word categorization and the text categorization is expected by combining both of them with each other. In this research, we define the similarity metric between two vectors, including the feature similarity, modify the KNN algorithm by replacing the exiting similarity metric by the proposed one, and apply it to the word categorization. The proposed KNN is empirically validated as the better approach in categorizing words in news articles and opinions. The significance of this research is to improve the classification performance by utilizing the feature similarities.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.199-202
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• 2007

The similarity measure is constructed for non-convex fuzzy membership function using well known Hamming distance measure. Comparison with convex fuzzy membership function is carried out, furthermore characteristic analysis for non-convex function are also illustrated. Proposed similarity measure is proved and the usefulness is verified through example. In example, usefulness of proposed similarity is pointed out.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.187-190
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• 2007

In this paper, we introduce concepts of entropy and similarity measure of interval-valued intuitionistic fuzzy sets (IVIFSs), discuss their relationship between similarity measure and entropy of IVIFSs, show that similarity measure and entropy of IVIFSs can be transformed by each other based on their axiomatic definitions and give some formulas to calculate entropy and similarity measure of IVIFSs.

• Journal of the Korea Convergence Society
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• v.5 no.4
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• pp.155-161
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• 2014

We survey the relation of fuzzy entropy measure and similarity measure. Each measure represents features of data uncertainty and certainty between comparative data group. With the help of one-to-one correspondence characteristics, distance measure and similarity measure have been expressed by the complementary characteristics. We construct similarity measure using distance measure, and verification of usefulness is proved. Furthermore analysis of similarity measure from fuzzy entropy measure is also discussed.

• International Journal of Advanced Culture Technology
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• v.1 no.2
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• pp.1-6
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• 2013

In this paper, we evaluate performance of existing similarity measurement metric and propose a novel method using user's preferences information entropy to reduce MAE in memory-based collaborative recommender systems. The proposed method applies a similarity of individual inclination to traditional similarity measurement methods. We experiment on various similarity metrics under different conditions, which include an amount of data and significance weighting from n/10 to n/60, to verify the proposed method. As a result, we confirm the proposed method is robust and efficient from the viewpoint of a sparse data set, applying existing various similarity measurement methods and Significance Weighting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1252-1271
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• 2013

Automatic image annotation has become an increasingly important research topic owing to its key role in image retrieval. Simultaneously, it is highly challenging when facing to large-scale dataset with large variance. Practical approaches generally rely on similarity measures defined over images and multi-label prediction methods. More specifically, those approaches usually 1) leverage similarity measures predefined or learned by optimizing for ranking or annotation, which might be not adaptive enough to datasets; and 2) predict labels separately without taking the correlation of labels into account. In this paper, we propose a method for image annotation through collaborative similarity metric learning from dataset and modeling the label correlation of the dataset. The similarity metric is learned by simultaneously optimizing the 1) image ranking using structural SVM (SSVM), and 2) image annotation using correlated label propagation, with respect to the similarity metric. The learned similarity metric, fully exploiting the available information of datasets, would improve the two collaborative components, ranking and annotation, and sequentially the retrieval system itself. We evaluated the proposed method on Corel5k, Corel30k and EspGame databases. The results for annotation and retrieval show the competitive performance of the proposed method.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.8
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• pp.1293-1305
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• 2009

Despite the attention regarding the effects of brand extension, there is limited research focused on brand extension in the beauty industry. This study discusses whether the similarity of extensional types and brand fit has any effect on the brand attitude toward beauty brand extension. This study examines the changes in the brand attitude and finds the effect of the similarity of extensional types and brand fits on brand attitude toward beauty brand extension. In the experiment, 4 description type factorial designs were performed. A total of 114 females participated in the experiment that had an experience of visiting a beauty salon. The results are as follows. First, the similarity of an extensional product-type has a positive effect on attitude toward parent beauty brand, while the similarity of extensional service-type does not. Second, there are significant independent and interaction effects between similarity and brand fit, which reveal differential influences on attitudes toward an extended beauty brand. Attitudes toward parent and extended beauty brands were affected by the similarity of extensional types and brand fit.

• 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].

• Journal of Advanced Navigation Technology
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• v.16 no.3
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• pp.455-462
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• 2012

The loss due to caching replacement in the wireless mobile proxy caching structure has a significant effect on streaming QoS. This paper proposes a similarity-based caching loss minimization (SCLM) for minimizing the loss caused by the caching replacement. The proposed scheme divides object segments, and then it performs the similarity relation about them. Segments that perform the similarity relation generates similarity relation tree (SRT). The similarity is an important metric for deciding a relevance feedback, and segments that satisfy these requirements in the cache block for caching replacement. Simulation results show that the proposed scheme has better performance than the existing prefix caching scheme, segment-based caching scheme, and bi-directional proxy scheme in terms of QoS, average delayed startup ratio, cache throughput, and cache response ratio.