• The Journal of Society for e-Business Studies
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• v.16 no.4
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• pp.301-319
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• 2011

Blog plays an important role as new type of knowledge base distinguishing from traditional web resource. While information resources in their existing website dealt with a wide range of topics, information resources of the blog are concentrated in specific units of information depending on the user's interests and have the criteria of classification forresources published by tagging. In this research, we build a tag hierarchy utilizing title keywords and tags of the blog, and propose apost clustering methodology applying the tag hierarchy. We then generate the tag hierarchy reflected the relationship between tags and develop the tag clustering methodology according to tag similarity. In this paper, we analyze the possibility of applying the proposed methodology with real-world examples and evaluate its performances through developed prototype system.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.173-182
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• 2005

In this paper, we propose a landmark recognition method which is irrelevant to the camera viewpoint on the navigation for localization. Features in previous research is variable to camera viewpoint, therefore due to the wealth of information, extraction of visual landmarks for positioning is not an easy task. The proposed method in this paper, has the three following stages; first, extraction of features, second, learning and recognition, third, matching. In the feature extraction stage, we set the interest areas of the image. where we extract the corner points. And then, we extract features more accurate and resistant to noise through statistical analysis of a small eigenvalue. In learning and recognition stage, we form robust feature models by testing whether the feature model consisted of five corner points is an invariant feature irrelevant to viewpoint. In the matching stage, we reduce time complexity and find correspondence accurately by matching method using similarity evaluation function and Graham search method. In the experiments, we compare and analyse the proposed method with existing methods by using various indoor images to demonstrate the superiority of the proposed methods.

• Journal of Korea Technology Innovation Society
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• v.16 no.4
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• pp.1109-1133
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• 2013

In an effort to discover new technologies and to forecast social changes of technologies, a number of technology life-cycle models have been developed and employed. The hype cycle, a graphical tool developed by a consulting firm, Gartner, is one of the most widely used models for the purpose and it is recognised as a practical one. However, more research is needed on theoretical frames, relations and empirical practices of the model. In this study, hype cycle comparisons in Korean and global search websites were performed by means of web-search traffic which is proposed as an empirical measurement of public expectation, analysed in a specific product or country in previous researches. First, search traffic and market share for new cars were compared in Korea and the U.S. with a view to identifying differences between the hype cycles in the two countries about the same product. The results show the similarity between the two countries with the statistical significance. Next, comparative analysis between search traffic and supply rate for several products in Korea was conducted to check out their patterns. According to the analysis, all the products seem to be at the "Peak of inflated expectations" in the hype cycles and they are similar to one another in the hype cycle. This study is of significance in aspects of expanding the scope of hype cycle analysis with web-search traffic because it introduced domestic web-search traffic analysis from Naver to analyse consumers' expectations in Korea by comparison with that from Google in other countries. In addition, this research can help to explain social phenomina more persuasively with search traffic and to give scientific objectivity to the hype cycle model. Furthermore, it can contribute to developing strategies of companies, such as marketing strategy.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1439-1448
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• 2005

In this paper, after similarity of color information in above toro and geometry position analysis of important characteristic information in face and abstraction object that is inputted detects face area using comparison, describe about method to do user certification using ratio information and hair spring degree. Face abstraction algorithm that use color information has comparative advantages than face abstraction algorithm that use form information because have advantage that is not influenced facial degree or site etc. that tip. Because is based on color information, change of lighting or to keep correct performance because is sensitive about color such as background similar to complexion is difficult. Therefore, can be used more efficiently than method to use color information as that detect characteristic information of eye and lips etc. that is facial importance characteristic element except color information and similarity for each object achieves comparison. This paper proposes system that eye and mouth's similarity that calculate characteristic that is ratio red of each individual after divide face by each individual and is segmentalized giving weight in specification calculation recognize user confirming similarity through search. Could experiment method to propose and know that the awareness rate through analysis with the wave rises.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.743-750
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• 2003

In this paper, we propose a query expansion and weighting method for document filtering to increase precision of the result of Web search engines. Query expansion for document filtering uses ConceptNet, encyclopedia and documents of 10% high similarity. Term weighting method is used for calculation of query-documents similarity. In the first step, we expand an initial query into the first expanded query using ConceptNet and encyclopedia. And then we weight the first expanded query and calculate the first expanded query-documents similarity. Next, we create the second expanded query using documents of top 10% high similarity and calculate the second expanded query- documents similarity. We combine two similarities from the first and the second step. And then we re-rank the documents according to the combined similarities and filter off non-relevant documents with the lower similarity than the threshold. Our experiments showed that our document filtering method results in a notable improvement in the retrieval effectiveness when measured using both precision-recall and F-Measure.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.14-25
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• 2020

This research proposes a methodology to evaluate the rationality of passengers' route choice who make trips within Seoul metropolitan subway based on smart card data. The rationality of user route choice is divided into the degree of determinacy and similarity concepts as basic principle. Determinacy is the degree to which the route selected by the passenger is identical to the system optimal path. Similarity indicates the degree to which the route is similar to the system optimal path. The K-path search method is used for path enumeration, which allows for measurement of determinacy. To assess determinacy within similarity, transfer tag data of private operators is used. Consequently, the concept of similarity applied to the model is such that the passenger's path choice is identical to the path taken using the tag reader. Results show that the determinacy of appearance of the shortest path (K=1) is 90.4%, while the similarity of appearance as K=(2-10) is 7.9%, summing to 98.3%. This indicates that trips on the metropolitan subway network are being rationally explained. 1.7% of irrational trips are attributed to the unexplainable error term that occurs due to the diversity of passengers.

• Journal of KIISE
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• v.41 no.12
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• pp.1117-1125
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• 2014

Folksonomy data, which is derived from social tagging systems, is a useful source for understanding a user's intention and interest. Using the folksonomy data, it is possible to create an accurate user profile which can be utilized to build a personalized search system. However there are limitations in some of the traditional methods such as Vector Space Model(VSM) for user profiling and similarity computation. This paper suggests a novel method with graph-based user and document profile which uses the proximity information of query terms to improve personalized search. We demonstrate the performance of the suggested method by comparing its performance with several state-of-the-art VSM based personalization models in two different folksonomy datasets. The results show that the proposed model constantly outperforms the other state-of-the-art personalization models. Furthermore, the parameter sensitivity results show that the proposed model is parameter-free in that it is not affected by the idiosyncratic nature of datasets.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.19-29
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• 2013

In the current Internet environment where there is high space complexity of information, search engines aim to provide accurate information that users want. But content-based method adopted by most of search engines cannot be used as an effective tool in the current Internet environment. As content-based method gives different weights to each web page using morphological characteristics of vocabulary, the method has its drawbacks of not being effective in distinguishing each web page. To resolve this problem and provide useful information to the users, this paper proposes an evaluation method based on categories. Category-based evaluation method is to extend query to semantic relations and measure the similarity to web pages. In applying weighting to web pages, category-based evaluation method utilizes user response to web page retrieval and categories of query and thus better distinguish web pages. The method proposed in this paper has the advantage of being able to effectively provide the information users want through search engines and the utility of category-based evaluation technique has been confirmed through various experiments.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.1-11
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• 2009

Recently there has arisen concern in both the database community and the graphics society about data retrieval from large motion databases because the high dimensionality of motion data implies high costs. In this circumstance, finding an effective distance measure and an efficient query processing method for such data is a challenging problem. This paper presents an elaborate motion query processing system, SMoFinder (Similar Motion Finder), which incorporates a novel kinematic distance measure and an efficient indexing strategy via adaptive frame segmentation. To this end, we regard human motions as multi-linkage kinematics and propose the weighted Minkowski distance metric. For efficient indexing, we devise a new adaptive segmentation method that chooses representative frames among similar frames and stores chosen frames instead of all frames. For efficient search, we propose a new search method that processes k-nearest neighbors queries over only representative frames. Our experimental results show that the size of motion databases is reduced greatly (${\times}1/25$) but the search capability of SMoFinder is equal to or superior to that of other systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.