• Proceedings of the Safety Management and Science Conference
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• 2013.11a
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• pp.393-400
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• 2013

Globally, smart phones have been rapidly distributed, which has led to changes in people's life cycle. Most people who are under 60 are supposed to use smart phones. Additionally, as the ratio of people who are interested in physical exercise is increasing, some applications for smart phones can manage dividual's exercise with the web servers. However, most of them can only check how much individual works out and cannot compare other's body type and life environment. Moreover, users cannot share their own data with others. This paper proposed the system which can resolve those kinds of problems through data mining techniques. The suggested model will have ability to figure out the relation between body type and the amount of exercise, find out if his work is proper from the result of classification and can pick out the features which is common to people who have similar body type and the amount of workout by applying data mining techiques. This model also will be able to recommend the proper amount of workout to each individual in order that they keep good health state efficiently.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.149-157
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• 2010

As the use of the Internet has recently increased, the demand for opinion information posted on the Internet has grown. However, such resources only exist on the website. People who want to search for information on the Internet find it inconvenient to visit each website. This paper focuses on the opinion information extraction and analysis system through Web mining that is based on statistics collected from Web contents. That is, users' opinion information which is scattered across several websites can be automatically analyzed and extracted. The system provides the opinion information search service that enables users to search for real-time positive and negative opinions and check their statistics. Also, users can do real-time search and monitoring about other opinion information by putting keywords in the system. Proposed technologies proved to have outstanding capabilities in comparison to existing ones through tests. The capabilities to extract positive and negative opinion information were assessed. Specifically, test movie review sentence testing data was tested and its results were analyzed.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.253-258
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• 2010

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.68-79
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• 2011

This paper focuses on the opinion information extraction and analysis system through Web mining that is based on statistics collected from Web contents. That is, users' opinion information which is scattered across several websites can be automatically analyzed and extracted. The system provides the opinion information search service that enables users to search for real-time positive and negative opinions and check their statistics. Also, users can do real-time search and monitoring about other opinion information by putting keywords in the system. Proposing technique proved that the actual performance is excellent by comparison experiment with other techniques. Performance evaluation of function extracting positive/negative opinion information, the performance evaluation applying dynamic window technique and tokenizer technique for multilingual information retrieval, and the performance evaluation of technique extracting exact multilingual phonetic translation are carried out. The experiment with typical movie review sentence and Wikipedia experiment data as object as that applying example is carried out and the result is analyzed.

• Korean Journal of Oriental Medicine
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• v.14 no.3
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• pp.81-87
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• 2008

Sasanag Contitution Medicine(SCM) is the traditional medicine theory based on constitutional medicine in Korea. It is most import ant that a personal SCM type is determined accurately ahead of applying any Sasang treatments. For this, many researches have been studied to diagnose the SCM type using constitutional clinical data. The decision tree is a tree-structured data-mining methodology. Recently, in the Korean traditional medicine society, there have been several efforts to find diagnosing tools using the decision tree method. So, we developed a decision tree program based on web for analyzing constitutional clinical information. It can use various clinical data as input data, offer filtering function to select clinical data to be used. We can find useful factor to be influential on SCM types using this program.

• Knowledge Management Research
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• v.15 no.3
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• pp.169-188
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• 2014

Big data seems to change knowledge management system and method of enterprises to large extent. Further, the type of method for utilization of unstructured data including image, v ideo, sensor data a nd text may determine the decision on expansion of knowledge management of the enterprise or government. This paper, in this light, attempts to figure out the prediction model of demands and supply for big data market of Korea trough data mining decision making tree by utilizing text bit data generated for 3 years on web and SNS for expansion of form for knowledge management. The results indicate that the market focused on H/W and storage leading by the government is big data market of Korea. Further, the demanders of big data have been found to put important on attribute factors including interest, quickness and economics. Meanwhile, innovation and growth have been found to be the attribute factors onto which the supplier puts importance. The results of this research show that the factors affect acceptance of big data technology differ for supplier and demander. This article may provide basic method for study on expansion of analysis form of enterprise and connection with its management activities.

• Food Science of Animal Resources
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• v.33 no.4
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• pp.463-473
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• 2013

Through the development of efficient data collecting technologies like RFID, and inter-enterprise collaboration platforms such as web services, companies which participate in supply chains can acquire visibility over the whole supply chain, and can make decisions to optimize the overall supply chain networks and processes, based on the extracted knowledge from historical data collected by the visibility system. Although not currently active, the MeatWatch system has been developed, and is used in part for this purpose, in the imported beef distribution network in Korea. However, the imported beef distribution network is too complicated to analyze its various aspects using ordinary process analysis approaches. In this paper, we suggest a novel approach, called RFID-based supply chain process mining, to automatically discover and analyze the overall supply chain processes from the distributed RFID event data, without any prior knowledge. The proposed approach was implemented and validated, by using a case study of the imported beef distribution network in Korea. Specifically we demonstrated that the proposed approach can be successfully applied to discover supply chain networks from the distributed event data, to simplify the supply chain networks, and to analyze anomaly of the distribution networks. Such novel process mining functionalities can reinforce the capability of traceability services like MeatWatch in the future.

• 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.

• Asia pacific journal of information systems
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• v.14 no.4
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• pp.147-162
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• 2004

A major difference between on-line services and off-line services is in the quality of the service they provide. On-line services lack of dynamic interface between customers and service providers. Traditional studies on personalizing web services do not consider the intimacy level of customers to web services. This study suggests a web personalizing method to satisfy customers in on-line using intimacy theory, cluster analysis, and data mining. The goal of this study is to support customers in web with more intimate service. It also offers improved services to customers by personalizing web services dynamically.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.1-11
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• 2018

Recently, the volume of online shopping market continues to be fast-growing, that is important to provide customized service based on customer behavior evaluation analysis. The existing systems only provide analysis data on the profiles and behaviors of the consumers, and there is a limit to the processing in real time due to disk based mining. There are problems of accuracy and system performance problems to apply existing systems to web services that require real-time processing and analysis. Therefore, The system proposed in this paper analyzes the web click log streams generated in real time to calculate the concentration level of specific products and finds interested customers which are likely to purchase the products, and provides and intensive promotions to interested customers. And we verify the efficiency and accuracy of the proposed system.