• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.197-211
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• 2004

According to the increase of internet application, the marketing process as well as the research and survey, the education process, and administration of government are very depended on web bases. All kinds of goods and sales which are traded on the internet shopping malls are extremely increased. So, the necessity of automatically intelligent information system is shown, this system manages web site connected users for effective marketing. For the recommendation system which can offer a fit information from numerous web contents to user, we propose an automatic recommendation system which furnish necessary information to connected web user using statistical learning theory and bayesian fuzzy clustering. This system is called collaborative CRM in this paper. The performance of proposed system is compared with the other methods using real data of the existent shopping mall site. This paper shows that the predictive accuracy of the proposed system is improved by comparison with others.

• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.1 s.108
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• pp.109-120
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• 2005

This landscape design proposal was presented to a design competition for Osong Biohealth Technopolis Institute of Cheongwon Gun Chung Cheong Buk Do which was held by Ministry of Health and Welfare in March 2004. The site is located in. Osong Li, Kang Wei Myun, Cheonwon Gun, Chung Cheong Buk Do and has an area of $402,600m^2$. The judging criteria for landscape design set by the client could be articulated as follows: an environment friendly design respecting the surrounding environment, a functionally efficient site plan by clustering buildings with similar uses, a site plan having 'front yard' by locating buildings in rear areas toward existing 'groves'. The proposal set the main design concept of this project as 'clustering'. By doing that, existing grades and plants can be saved, buildings with similar uses can be clustered, huge 'front yard' as a symbolic image of this project can be achieved, and finally many small open spaces for everyday life can be designed accordingly.

• Journal of Information Technology Applications and Management
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• v.23 no.1
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• pp.33-43
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• 2016

Along with the spread of digital music and recent growth in the digital music industry, the demands for music recommender are increasing. These days, listeners have increasingly preferred to digital real-time streamlining and downloading to listen to music because it is convenient and affordable for the listeners to do that. We use Bayesian learning through weight of listener's prefered music site such as Melon, Billboard, Bugs Music, Soribada, and Gini. We reflect most popular current songs across all genres and styles for music recommender system using user profile. It is necessary for us to make the task of preprocessing of clustering the preference with weight of listener's preferred music site with popular music charts. We evaluated the proposed system on the data set of music sites to measure its performance. We reported some of the experimental result, which is better performance than the previous system.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.59-69
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• 2015

Many users visit websites every day to perform information retrieval, shopping, and community activities. On the other hand, there is intense competition among sites which attempt to profit from the Internet users. Thus, the owners or marketing officers of each site try to design a variety of marketing strategies including cooperation with other sites. Through such cooperation, a site can share customers' information, mileage points, and hyperlinks with other sites. To create effective cooperation, it is crucial to choose an appropriate partner site that may have many potential customers. Unfortunately, it is exceedingly difficult to identify such an appropriate partner among the vast number of sites. In this paper, therefore, we devise a new methodology for recommending appropriate partner sites to each site. For this purpose, we perform site clustering from the perspective of visitors' similarities, and then identify a group of sites that has a number of common customers. We then analyze the potential for the practical use of the proposed methodology through its application to approximately 140 million actual site browsing histories.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Safety and Health at Work
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• v.3 no.4
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• pp.278-286
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• 2012

Objectives: Authors investigated the pattern of the rate of occupational injuries and illnesses (ROII) at the level of enterprises in order to build a network for exchange of experience and knowledge, which would contribute to workers' safety and health through safety climate of workplace. Methods: Occupational accidents were analyzed at the manufacturing work site unit. A two step clustering process for the past patterns regarding the ROII from 2001 to 2009 was investigated. The ROII patterns were categorized based on regression analysis and the patterns were further divided according to the subtle changes with Mahalanobis distance and Ward's linkage. Results: The first clustering of ROII through regression analysis showed 5 different functions; 29 work sites of the linear function, 50 sites of the quadratic function, 95 sites of the logarithm function, 62 sites of the exponential function, and 54 sites of the sine function. Fourteen clusters were created in the second clustering. There were 3 clusters in each function categorized in the first clustering except for sine function. Each cluster consisted of the work sites with similar ROII patterns, which had unique characteristics. Conclusion: The five different patterns of ROII suggest that tailored management activities should be applied to every work site. Based on these differences, the authors selected exemplary work sites and built a network to help the work sites to share information on safety climate and accident prevention measures. The causes of different patterns of ROII, building network and evaluation of this management model should be evaluated as future researches.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.393-398
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• 2001

특정 영역에 대해 사용자에게 관련 정보를 제공해 주는 서비스를 하는 정보 에이전트를 개발 중이다. 정보 에이전트는 사용자 질의 처리를 달은 Agent Manager와 지식베이스를 관리하는 KB Manager, 그리고 Web으로부터 해당 영역의 관련 문서를 끌어오는 Web Manager로 구성되어 있다. Web Manager는 방문할 URL을 수집하고, 이들 문서에 대한 관련 평가와 Indexing을 수행한다. Web Manager는 검색 엔진을 이용하거나, 방문한 문서의 link를 이용하여 URL을 수집하는데 이러한 URL수집기법은 많은 관련 문서를 놓치는 문제점이 있다. 이 문제점을 해결하기 위해서 해당 영역과 관련된 Site들을 대상으로 Link를 이용해 문서들을 모아와, 문서들을 TAG들의 패턴으로 얻어낸 문서 형식을 이용해 Clustering하며 관련 문서들의 Group을 찾아내는 적극적 문서 수집 기법을 제안한다. 실험 결과, Link와 Clustering을 이용할 경우 기존보다 효과적으로 관련 문서를 많이 수집할 수 있음을 알 수 있다.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.305-316
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• 2021

The objective is to recognize the better opportunities from targeted reveal advertising, to show a banner ad to the consumer of online who is most expected to obtain a preferred action like signing up for a newsletter or buying a product. Discovering the most excellent commercial impression, it means the chance to exhibit an advertisement to a consumer needs the capability to calculate the probability that the consumer who perceives the advertisement on the users browser will acquire an accomplishment, that is the consumer will convert. On the other hand, conversion possibility assessment is a demanding process since there is tremendous data growth across different information dimensions and the adaptation event occurs infrequently. Retailers and manufacturers extensively employ the retail services from internet as part of a multichannel distribution and promotion strategy. The rate at which web site visitors transfer to consumers is low for online retail, out coming in high customer acquisition expenses. Approximately 96 percent of web site users concluded exclusive of no shopper purchase[1].This category of conversion rate is collected from the advertising of social media sites and pages that dataset must be estimating and assessing with the concept of big data clustering, which is used to group the particular age group of people along with their behavior. This makes to identify the proper consumer of the production which leads to improve the profitability of the concern.

• Spatial Information Research
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• v.8 no.2
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• pp.301-315
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• 2000

Every, year, a great amount of site investigation data is collected on site to obtain sufficient conditions. Investigation of subsurface conditions is prerequisite to the design and construction of structures and also provides information on ground properties such as geologic formation and types of soil. This data set, which portrays real representation of ground conditions over the existing geologic and soil maps, could be further utilized for analyzing the subsurface conditions. It is therefore necessary to develope a subsurface exploration analysis system which is able to extract the valuable information from the heterogeneous, non-normalized subsurface investigation data. This paper presents the overall design scheme and implementation on a subsurface exploration analysis system. The analysis system employs one of data set such as bore-hole data. The clustering technique employed in the developed system makes a large volume of bore-hole data into several groups in terms of ground formation and geographical vicinity. As a result of clustering, each group or cluster consists of bore-hole data with similar characteristics of subsurface and geographical vicinity. In addition, each clustered data is displayed on digital topographical map with different color so that the analysis of site investigation data could be performed in more sensible ways.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.322-325
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• 2002

We proposed a method to improve KCN's problems. Proposed method adjusts neighborhood and teaming rate by fuzzy logic system. The input of fuzzy logic system used a distance and a change rate of distance. The output was used by site of neighborhood and learning rate. The rule base of fuzzy logic system was taken by using KCN simulation results. We used Anderson's Iris data to illustrate this method, and simulation results showed effect of performance.