• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.65-78
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• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.365-380
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• 2002

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from large quantities of temporal data. Temporal knowledge, expressible in the form of rules, is knowledge with temporal semantics and relationships, such as cyclic pattern, calendric pattern, trends, etc. There are many examples of temporal data, including patient histories, purchaser histories, and web log that it can discover useful temporal knowledge from. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering temporal knowledge from temporal data, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treated data in database at best as data series in chronological order and did not consider temporal semantics and temporal relationships containing data. In order to solve this problem, we propose a theoretical framework for temporal data mining. This paper surveys the work to date and explores the issues involved in temporal data mining. We then define a model for temporal data mining and suggest SQL-like mining language with ability to express the task of temporal mining and show architecture of temporal mining system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.61-75
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• 2011

Recently, there is significant increasing of massive attacks, which try to infect PCs that visit websites containing pre-implanted malicious code. When visiting the websites, these hidden malicious codes can gain monetary profit or can send various cyber attacks such as BOTNET for DDoS attacks, personal information theft and, etc. Also, this kind of malicious activities is continuously increasing, and their evasion techniques become professional and intellectual. So far, the current signature-based detection to detect websites, which contain malicious codes has a limitation to prevent internet users from being exposed to malicious codes. Since, it is impossible to detect with only blacklist when an attacker changes the string in the malicious codes proactively. In this paper, we propose a novel approach that can detect unknown malicious code, which is not well detected by a signature-based detection. Our method can detect new malicious codes even though the codes' signatures are not in the pattern database of Anti-Virus program. Moreover, our method can overcome various obfuscation techniques such as the frequent change of the included redirection URL in the malicious codes. Finally, we confirm that our proposed system shows better detection performance rather than MC-Finder, which adopts pattern matching, Google's crawling based malware site detection, and McAfee.

• Journal of KIISE
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• v.41 no.10
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• pp.838-846
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• 2014

Three-dimensional (3D) object retrieval from user-drawn sketch queries is one of the important research issues in the areas of pattern recognition and computer graphics for simulation, visualization, and Computer Aided Design. The performance of content-based 3D object retrieval system depends on the availability of effective descriptors and similarity measures for this kind of data. In this paper, we present a sketch-based 3D object retrieval system by extracting a hybrid edge descriptor which is robust against rotation and translation. The experimental results which are based on HTML5 and WebGL show that proposed sketch-based 3D object retrieval method is very efficient to search and order the 3D objects according to user's intention.

• Journal of The Korean Association of Information Education
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• v.9 no.4
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• pp.607-616
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• 2005

The art education in elementary schools pursued by the 7th education course is to nurture more aesthetic human who can live a more beautiful life by applying the beauty they felt and saw, rather than wishing all students to become artists. In other words, it emphasizes on the importance of appreciation activity. However, the reality is that technique-oriented learning is the main in the education field, especially around the academies, and appreciation learning is even more difficulty for students at physical spaces such as galleries who live inisland or distant countries. This study designs and materializes a system providing appreciation learning data in the form of gallery based on 3 dimensional learning pattern, which is similar to form of human senses, to solve the demerits of former appreciation resources being shared around a small number of students.

• Journal of IKEEE
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• v.14 no.1
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• pp.17-24
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• 2010

This paper presents calorie expenditure prediction model of daily activity of elderly living alone for LBS(Location Based Service) applications. The proposed method is to describe the daily activity patterns of older adult using PIR (Passive InfraRed) motion sensors and to examine the relationships between physical activity and calorie expenditure. The developed motion detecting system is composed of a sensing system and a server system. The motion detecting system is a set of wireless sensor nodes which has PIR sensor to detect a motion of elder. Each sensing node sends its detection signal to a home gateway via wireless link. The home gateway stores the received signals into a remote database. The server system is composed of a database server and a web server, which provides web-based monitoring system to caregivers for more effective services. The experiment results show the adaptability and feasibility of the calorie expenditure model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.608-613
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• 2006

There are many e-showing mall because of revitalization of e-commerce system. It is necessary to recommending system of products that is for saving time and effort of customer. In this paper, we propose the system that is applying classification among data mining techniques to analysis of log data of customer. This log data contains access of user and purchasing of products. The proposed system operates in two phases. The first phase is composed of data filter module and association extraction module among web pages. The second phase is composed of personalization module and rule generation module. Customer can easily know the recommended sites because the proposed system can present rank of the recommended web pages to customer. As a result, the proposed system can efficiently do recommending of products to customer.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.29-37
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• 2011

Recently, many user-participating web services have been used widely as the evolution of internet web technology has rapidly been developed. Users share various content and opinion on line using a site like ‘Social bookmarking.’ Users can share others’ bookmarking history and create tags while bookmarking web sites; we call it collaborative tagging. In this paper, we studied empirical analysis for widely used social bookmarking and collaborative tagging which the result shows minority of users is actively using the bookmarking and a few sites and tags are used by majority of the users. 24% users tagged 80%, 75% sites and 81% tags were tagged below than 3 times. Types of bookmarking activities were found different by users and early appointed tags get more frequency by majority. We also identified relative proportions of tags on certain sites are becoming convergence gradually. We expect the result of this paper will give opportunities to help further developing social bookmarking system.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.385-392
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• 2002

Collaborative filtering method for personalization can suggest new items and information which a user hasn t expected. But there are some problems. Not only the steps for calculating similarity value between each user is complex but also it doesn t reflect user s interest dynamically when a user input a query. In this paper, classifying users by their interest makes calculating similarity simple. We propose the a1gorithm for readjusting user s interest dynamically using the profile and Bayesian learning. When a user input a keyword searching for a item, his new interest is readjusted. And the user s profile that consists of used key words and the presence frequency of key words is designed and used to reflect the recent interest of users. Our methods of adjusting user s interest using the profile and Bayesian learning can improve the real satisfaction of users through the experiment with data set, collected in University s library. It recommends a user items which he would be interested in.

• Journal of Global Scholars of Marketing Science
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• v.11
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• pp.129-146
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• 2003

This study explores whether a NBD type of model can be applied to characterize the underlying frequency distribution of online consumer's visit behavior. In this study, the following two research questions are addressed: (1) How can we characterize the underlying distribution pattern(s) of the number of repeat i i visits to a site? (2) How can consumer's Internet usages and his/her demographics affect the average number of visits to the site? Through the empirical investigation, this study found that NBD models are directly applicable to characterize the underlying distribution of visit frequency on the Internet. Furthermore, this study addresses some managerial implications for understanding how site visits are determined. Especially this study highlights the relationship between repeated visits and the visitors' Internet Usages and demographics. The proposed models are estimated and validated by online panel data that covers more than 1000 different sites and has 800,000 observations.