• Journal of the Korean Home Economics Association
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• v.43 no.3 s.205
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• pp.1-10
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• 2005

The purpose of this study was to investigate the enforcement strategy for Consumer Service marketing of an e-Commerce shopping mall. An e-CRM for a Cosmetic e-Commerce shopping mall, Data Warehousing(DW) component, analysis of data mining of the DW, and web applications and strategies had to developed for marketing of consumer service satisfaction. The major findings were as follows: An RFM analysis was used for consumer classification, which is a fundamental process of e-CRM application. The components of the DW were web sales data and consumer data fields. The visual process of consumer segmentations (superior consumer class) for e-CRM solutions is presented. The association analysis algorithm of data mining to up-selling and cross-selling indicates an association rule. These e-CRM results apply web DB marketing and operating principles to a shopping mall. Therefore, the system applications of e-CRM to Consumer services indicate a marketing strategy for consumer-oriented management.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.91-95
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• 2007

Main goal of web recommendation system is to study how user behavior on a website can be predicted by analyzing web log data which contain the visited web pages. Many researches of the web recommendation system have been studied. To construct web recommendation system, web mining is needed. Especially, web usage analysis of web mining is a tool for recommendation model. In this paper, we propose web recommendation system using grid based support vector machines for improvement of web recommendation system. To verify the performance of our system, we make experiments using the data set from our web server.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.759-772
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• 2003

K-Means and Web mining modelling have been tried for finding major DNA marker of BM4311 microsatellite loci in Hanwoo Chromosome 6 linkage map. Furthermore, a major DNA mining by bootstrap simulations(BCa) has been applied.

• The Journal of Bigdata
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• v.2 no.2
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• pp.105-114
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• 2017

Today, many services are supported on the web sites. Analysis of usage patterns of web site visitors is very important to optimize the use and efficiency of the web sites. In this study, analysis of usage patterns and comparative analysis of user groups were conducted by analyzing web access log provided by BPI Challenge 2016. This data provides access logs to the web site in the IT system of a Dutch Employee Insurance Agency (UWV). The customer information, and the click data describing the customers' behavior when using the agency's web site. In this study, we use process mining techniques to analyze the usage patterns of customers and the characteristics of customer groups, and ultimately improve the service quality of customers using web services.

• Asia pacific journal of information systems
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• v.12 no.3
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• pp.71-88
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• 2002

In this paper, intelligent marketing and merchandising methods utilizing data mining and Web mining techniques are proposed for online retailers to survive and succeed in gaining competitive advantage in a highly competitive environment. The first part of this paper explains the procedures of one-to-one marketing based on customer relationship management(CRM) techniques and personalized recommendation lists generation. The second part illustrates Web merchandising methods utilizing data mining techniques, such as association and sequential pattern mining. We expect that our Web marketing and merchandising methods will both provide a currently operating Internet shopping mall with more selling opportunities and give more useful product information to customers.

• Journal of Korea Multimedia Society
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• v.8 no.9
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• pp.1163-1176
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• 2005

The Web is the largest distributed information space but, the individual's capacity to read and digest contents is essentially fixed. In these Web environments, mining traversal patterns is an important problem in Web mining with a host of application domains including system design and information services. Conventional traversal pattern mining systems use the inter-pages association in sessions with only a very restricted mechanism (based on vector or matrix) for generating frequent K-Pagesets. We extend a family of novel algorithms (termed WebPR - Web Page Recommend) for mining frequent traversal patterns and then pageset to recommend. We add a WebPR(A) algorithm into a family of WebPR algorithms, and propose a new winWebPR(T) algorithm introducing a window concept on WebPR(T). Including two extended algorithms, our experimentation with two real data sets, including LadyAsiana and KBS media server site, clearly validates that our method outperforms conventional methods.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.177-191
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• 2003

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is the most successful recommendation technology. Web usage mining and clustering analysis are widely used in the recommendation field. In this paper, we propose several hybrid collaborative filtering-based recommender procedures to address the effect of web usage mining and cluster analysis. Through the experiment with real e-commerce data, it is found that collaborative filtering using web log data can perform recommendation tasks effectively, but using cluster analysis can perform efficiently.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.101-107
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• 2018

Visitors to a Web site leave access logs documenting their activity in the site. These access logs provide a valuable source of information about the visitors' access patterns in the Web site. In addition to the pages that the user visited, it is generally possible to discover the geographical locations of the visitors. Web servers also records other information such as the entry into the site, the URL, the used operating system and the browser, etc. There are several Web mining techniques to extract useful information from such information and visualization of a Web log is one of those techniques. This paper presents a technique as well as a case a study of visualizing a Web log.

• Journal of Information Technology Applications and Management
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• v.14 no.3
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• pp.137-149
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• 2007

Most of hospitals, especially small-scale hospitals, have tried to get customers through the Internet as what companies have done recently. There are various attempts that increase visits to one's web-site in plastic surgery hospitals. However, in plastic surgery, there have been few studies on which an attribute contributes to increase the number of web-site visit. In order to derive the important attributes on the number of visit, we compared functional attributes of 30 high-visit plastic surgery web-sites with those of 30 low-visit web-sites using statistical and data mining methods. For analysis, three methods have conducted including Multiple Discriminant Analysis (statistical method), Decision Trees (data mining method), and Artificial Neural Network (data mining method). Furthermore, results of each method have been evaluated one another. The result of this study shows that a few attributes like 'Simulating cyber plastic surgery program', 'recommendation of information' explain the number of the visitors between high and low visit web-site. The methodology employed in this study provides an efficient way of improving satisfaction of visitors of plastic surgery website.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.299-308
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• 2006

As recently XML is becoming the standard of exchanging web documents and public documentations, XML data are increasing in many areas. To retrieve the information about XML documents efficiently, the semantic web based on the ontology is appearing. The existing ontology has been constructed manually and it was time and cost consuming. Therefore in this paper, we propose the semi-automatic ontology generation technique using the data mining technique, the association rules. The proposed method solves what type and how many conceptual relationships and determines the ontology domain level for the automatic ontology generation, using the data mining algorithm. Appying the association rules to the XML documents, we intend to find out the conceptual relationships to construct the ontology, finding the frequent patterns of XML tags in the XML documents. Using the conceptual ontology domain level extracted from the data mining, we implemented the semantic web based on the ontology by XML Topic Maps (XTM) and the topic map engine, TM4J.