• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.297-304
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• 2003

Nowdays, with the explosive growth of the web information, web users Increase requests of systems which collect and analyze web pages that are relevant. The systems which were develop to solve the request were used clustering methods to improve the duality of information. Clustering is defining inter relationship of unordered data and grouping data systematically. The systems using clustering provide the grouped information to the users. So, they understand the information efficiently. We proposed a hybrid clustering method to cluster a large quantity of data efficiently. By that method, We generate initial clusters using COBWEB Algorithm and refine them using Ezioni Algorithm. This paper adds two ideas in prior hybrid clustering method to increment accuracy and efficiency of clusters. Firstly, we propose the clustering method considering weight of attributes of data. Second, we redefine evaluation functions which generate initial clusters to increase efficiency in clustering. Clustering method proposed in this paper processes a large quantity of data and diminish of dependancy on sequence of input of data. So the clusters are useful to make user profiles in high quality. Ultimately, we will show that the proposed clustering method outperforms the pervious clustering method in the aspect of precision and execution speed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.992-998
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• 2017

For local advertising based on D2D communications, sources want advertisement messages to be diffused to unspecified users as many as possible. It is one of challenging issues to select target-areas for advertising if users are uniformly distributed. In this paper, we propose D2D based advertisement dissemination algorithm using user clustering with expectation-maximization. The user distribution of each cluster can be estimated by principal components (PCs) obtained from each cluster. That is, PCs enable the target-areas and routing paths to be properly determined according to the user distribution. Consequently, advertisement messages are able to be disseminated to many users. We evaluate performances of our proposed algorithm with respect to coverage probability and average reception number per user.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1441-1446
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• 2022

Modern people can access various information about food recipes very easily on the Internet or social media. As the supply of food recipes increases, it is difficult to find a suitable recipe for each user in the overflowing information. As such, the need to provide information by reflecting users' requirements has increased, and research related to food recipes and cooking recommendations is becoming active. In addition, the Internet, video, and application markets using this are also rapidly activating. In this study, in order to classify recipes from the user's perspective of food recipe users, the user's review data was applied with the k-mean clustering technique, which is unsupervised learning, and a "food recipe classification model" was derived. As a result, it was classified into a total of 25 clusters including information needed by many users, such as specific purposes and cooking stages.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.4
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• pp.103-111
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• 2020

Perforated metal sheets are used as panels of facades for controlling environmental factors while ensuring user's visibility. Despite their functional potentials, only a specific direction of facades or an orientation of a building was considered in the relevant studies. This study proposed a design methodology for the perforated panel facades that reflects the location on the facades and the user's requirements. The optimization of quantitative and qualitative performance is achieved through communication between designers and users in a VR system. In optimizing quantitative performances, designers use machine learning techniques such as clustering and genetic algorithm to allocate optimal panels on the facades. In optimizing qualitative performances, through the VR system, users intervene in evaluating performances whose preferences are depending on them. The experiment using the office project showed that designers were able to make decisions based on clustering using GMM to optimize multiple quantitative performances. The gap between the target and final performance could be narrowed by limiting the types of perforated panels considering mass customization. In assessing visibility as a qualitative performance, users were able to participate in the design process using the VR system.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.711-716
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• 2006

Since the introduction of digital camera to the mass market, the number of digital photos owned by an individual is growing at an alarming rate. This phenomenon naturally leads to the issues of difficulties while searching and browsing in the personal digital photo archive. Traditional approach typically involves content-based image retrieval using computer vision algorithms. However, due to the performance limitations of these algorithms, at least on the casual digital photos taken by non-professional photographers, more recent approaches are centered on time-based clustering algorithms, analyzing the shot times of photos. These time-based clustering algorithms are based on the insight that when these photos are clustered according to the shot-time similarity, we have "event clusters" that will help the user browse through her photo archive. It is also reported that one of the remaining problems with the time-based approach is that people perceive events in different scales. In this paper, we present an adaptive time-based clustering algorithm that exploits the usage history of digital photos in order to infer the user's preference on the event granularity. Experiments show significant performance improvements in the clustering accuracy.

• Journal of the Korea Computer Graphics Society
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• v.16 no.4
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• pp.35-40
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• 2010

Due to the distribution of digital camera, many work for photo management has been studied. However, most work use a sequential grid layout which arranges photos considering one criterion of digital photo. This interface makes users have lots of scrolling and concentrate ability when they manage their photos. In this paper, we propose a clustering method based on a temporal sequence considering their color similarity in detail. First we cluster photos using Cooper's event clustering method. Second, we makes more detailed clusters from each clustered photo set, which are clustered temporal clustering before, using maximal clique finding algorithm of interval graph. Finally, we arrange each detailed dusters on a user screen with their overlap keeping their temporal sequence. In order to evaluate our proposed system, we conducted on user studies based on a simple questionnaire.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.101-107
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• 2014

This paper presents an efficient distributed recommendation system using clustering collaborative filtering algorithm in distributed computing environments. The system was built based on Hadoop distributed computing platform, where distributed Min-hash clustering algorithm is combined with user based collaborative filtering algorithm to optimize recommendation performance. Experiments using Movie Lens benchmark data show that the proposed system can reduce the execution time for recommendation compare to sequential system.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.223-226
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• 2003

Because UMLS semantic network is bulky and complex, user hard to understand and has shortcoming that can not express all semantic network on screen. To solve this problem, rules to dismember semantic network efficiently are introduction. but there is shortcoming that this should classifies manually applying rule whenever UMLS semantic network is modified. Suggest automatic clustering method of UMLS semantic network that use genetic algorithm to solve this problem. Proposed method uses Linked semantic relationship between each semantic type and semantic network does clustering by structurally similar semantic type linkages. To estimate the performance of suggested method, we compared it with result of clustering method by rule.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.909-922
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• 2000

High sped messaging layer for application's feeling of low level network performance is needed by Clustering System based on high speed network fabrics. It should have the mechanism to directly pass messages between network card and application space, and provide flexible affodabilities for many diverse applications. In this paper, CROWN (Clustering Resources On Workstations' Network) which is designed and implemented for multi-purpose clustering system will be introduced briefly, and CLCP(CROWN Lean Communication Primitives)which is the high speed messaging layer for CROWN will be followed. CLCP consists of a firmware for controlling Myrinet card, device drier, and user libraries. CLCP supports various application domains as a result of pooling and interrupt receive mechanism. In case of polling based receive, 8 bytes short message, and no other process, CLCP has 262 micro-second response time between two nodes, and IM bytes large message, it shows 442Mbps bandwidth.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.163-177
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• 2021

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users' interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster's nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.