• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.408-411
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• 2006

Currently, the nano economy threatens the mass economy. This is based on the internet business models. In the nano business models based on internet, the diversely personalized services are needed. Many researches of the personalization on the web have been studied. The web usage mining using click stream data is a tool for personalization model. In this paper, we propose an internet business model using evolutionary support vector machine and web response survey as a web usage mining. After analyzing click stream data for web usage mining, a personalized service model is constructed in our work. Also, using an approach of web response survey, we improve the performance of the customers' satisfaction. From the experimental results, we verify the performance of proposed model using two data sets from KDD Cup 2000 and our web server.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.843-849
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• 2003

Web usage mining is the technique of data mining that analyzes web users' usage patterns by large web log. To use the web usage mining technique, we have to classify correctly users and users session in preprocessing, but can't classify them completely by only log files with standard web log format. To classify users and user session there are many problems like local cache, firewall, ISP, user privacy, cookey etc., but there isn't any definite method to solve the problems now. Especially local cache problem is the most difficult problem to classify user session which is used as input in web mining systems. In this paper we propose a heuristic method which solves local cache problem by using only click stream data of server side like referrer log, agent log and access log, classifies user sessions and completes session.

• International Journal of Advanced Culture Technology
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• v.4 no.2
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• pp.7-18
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• 2016

The first D2C-era web-real variety show in Korea was broadcast via tvN of CJ E&M. The web-real variety program 'SinSeoYuGi' accumulated 54 million views, along with 50 million views at the Chinese portal site QQ. This study carries out an analysis using text mining that extracts portal site blogs, twitter page views and associative terms. In addition, this study derives viewers' response by extracting key words with opinion mining techniques that divide positive words, neutral words and negative words through customer sentiment analysis. It is found that the success factors of the web-real variety were reduced in appearance fees and production cost, harmony between actual cast members and scenario characters, mobile TV programing, and pre-roll advertising. It is expected that web-real variety broadcasting will increase in value as web contents in the future, and be established as a new genre with the job of 'technical marketer' growing as well.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.187-198
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• 2004

The World-Wide Web is the largest distributed Information space and has grown to encompass diverse information resources. However, although Web is growing exponentially, the individual's capacity to read and digest contents is essentially fixed. From the view point of Web users, they can be confused by explosion of Web information, by constantly changing Web environments, and by lack of understanding needs of Web users. 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 develop a family of novel algorithms (termed WebPR - Web Page Recommend) for mining frequent traversal patterns and then pageset to recommend. Our algorithms provide Web users with new page views, which Include pagesets to recommend, so that users can effectively traverse its Web site. The main distinguishing factors are both a point consistently spanning schemes applying inter-pages association for mining frequent traversal patterns and a point proposing the most efficient tree model. Our experimentation with two real data sets, including Lady Asiana and KBS media server site, clearly validates that our method outperforms conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.113-119
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• 2008

The World Wide Web is a dynamic collection of pages that includes a huge number of hyperlinks and huge volumes of usage informations. The resulting growth in online information combined with the almost unstructured web data necessitates the development of powerful web data mining tools. Recently, a number of approaches have been developed for dealing with specific aspects of web usage mining for the purpose of automatically discovering user profiles. We analyze sequence data, such as web-logs, protein sequences, and retail transactions. In our approach, we propose the clustering algorithm for sequence data using rough set theory. We present a simple example and experimental results using a splice dataset and synthetic datasets.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.5
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• pp.236-244
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• 2006

Many attempts have been made to track the web usage patterns and provide suggestions that might help web operators get the information they need. These tracking mechanisms rely on mining web log files for usage patterns. The purpose of this study is to verify a web agent prototype that was built for mining web log files. The web agent for this paper was made by Java and ASP and the agent came into being as part of a cookie for a short-term data storage. For long-term data storage, the agent used a My-SQL as a Data Base. This agent system could inform that if the data comes from the web data mining agent, it could be a rapid information providing method rather than the case of data coming into a data mining tool. Therefore, the developed tool in this study will be helpful as a new kind of decision making system and expert system.

• The Journal of Society for e-Business Studies
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• v.8 no.3
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• pp.53-67
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• 2003

The importance of web Mining is highlighted with growth of Electronic Commerce. Web Mining is the important field of subject for studying customer's purchasing trend in Electronic Commerce. This research defined customer's purchasing process as Fuzzy environment in Electronic Commerce. And it suggests new methodology that introduces Fuzzy theory based on current Web Mining methodology

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.3-5
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• 2006

Recent advances in the internet infrastructure have resulted in a large number of huge Web sites and portals worldwide. These Web sites are being visited by various types of users in many different ways. Among all the web page access sequences from different users, some of them occur so frequently that may need an attention from those who are interested. We call them frequent access patterns and access sequences that can be frequent the candidate patterns. Since these candidate patterns play an important role in the incremental Web mining, it is important to efficiently generate, add, delete, and search for them. This thesis presents a novel tree structure that can efficiently store the candidate patterns and a related set of algorithms for generating the tree structure adding new patterns, deleting unnecessary patterns, and searching for the needed ones. The proposed tree structure has a kind of the 3 dimensional link structure and its nodes are layered.

• IE interfaces
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• v.16 no.1
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• pp.54-62
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• 2003

With the ubiquitous use of the Internet in daily business activities, most of modern firms are keenly interested in customer's behaviors on the Internet. That is because a wide variety of information about customer's intention about the target web site can be revealed from IP address, reference address, cookie files, duration time, all of which are expressing customer's behaviors on the Internet. In this sense, this paper aims to accomplish an objective of analyzing a set of exemplar web log files extracted from a specific P2P site, anti identifying information about customer segmentation strategies. Major web mining technique we adopted includes a machine learning like C5.0.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.342-351
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• 2002

A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies.