• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.443-446
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• 2002

In this research, we suggest a Web-based hybrid recommendation mechanism using CBR (Case-Based Reasoning) and web data mining. Data mining is used as an efficient mechanism in reasoning for relationship between goods, customers' preference and future behavior. CBR systems are normally used in problems for which it is difficult to define rules. We use CBR as an AI tool to recommend the similar purchase case. A Web-log data gathered in real-world Internet shopping mall was given to illustrate the quality of the proposed mechanism. The results showed that the CBR and web data mining-based hybrid recommendation mechanism could reflect both association knowledge and purchase information about our former customers.

• Management Science and Financial Engineering
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• v.15 no.2
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• pp.101-118
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• 2009

In quantitative contexts, data mining is widely applied to the prediction of stock prices from financial time-series. However, few studies have examined the potential of data mining for shedding light on the qualitative problem-solving knowledge of experts who make stock price predictions. This paper presents a GA-based data mining approach to characterizing the qualitative knowledge of such experts, based on their observed predictions. This study is the first of its kind in the GA literature. The results indicate that this approach generates rules with higher accuracy and greater coverage than inductive learning methods or neural networks. They also indicate considerable agreement between the GA method and expert problem-solving approaches. Therefore, the proposed method offers a suitable tool for eliciting and representing expert decision rules, and thus constitutes an effective means of predicting the stock price index.

• Proceedings of the Korea Database Society Conference
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• 1997.10a
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• pp.215-269
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• 1997

Outline $\textbullet$ Introduction $\textbullet$ Multimedia - Types of Data - Motivation - Key issue - Hardware Products - Application Areas $\textbullet$ Agents - Rationale for Agents - Sedentary vs. Mobile - Functional Categories - Application Areas $\textbullet$ Data Mining - 2-D Framework for Data Mining Tools - Classification of Tool - Application Areas - Learning Methodologies ＊ Case Based Reasoning ＊ Neural Networks ＊ Statistical Learning： Orthogonal Arrays ＊ Multi-strategy Learning $\textbullet$ Case Study - Finbot $\textbullet$ Conclusion

• Journal of Engineering Education Research
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• v.19 no.2
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• pp.53-59
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• 2016

Along with the industry growth, engineering colleges in Korea has have a quantitative growth. Many of the policy promotions and budgets for engineering colleges from the government are supported. And the various monitoring methods to verify their achievement have demanded. This paper deals with the construction of engineering educational statistics system in Korea. It named Korea Engineering Data Management System(K-EDMS). This system is based on the data mining tool and supports data-based decision making for an advanced engineering education service. This paper presents related researches of case studies. Then, we have designed K-EDMS, and constructed 157 cases for engineering colleges of the year 2014.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.4
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• pp.460-469
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• 2008

Process mining aims at mining valuable information from process execution results (called "event logs"). Even though process mining techniques have proven to be a valuable tool, the mining results from real process logs are usually too complex to interpret. The main cause that leads to complex models is the diversity of process logs. To address this issue, this paper proposes a trace clustering approach that splits a process log into homogeneous subsets and applies existing process mining techniques to each subset. Based on log profiles from a process log, the approach uses existing clustering techniques to derive clusters. Our approach are implemented in ProM framework. To illustrate this, a real-life case study is also presented.

• Asia pacific journal of information systems
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• v.25 no.1
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• pp.83-97
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• 2015

Recently, the exhibition industry has developed rapidly with the development of information technologies. Most exhibitors in an exhibition plan and deploy many events that may provide advantages to visitors as a method of effective promotion. The growth and propagation of wireless technologies is a powerful marketing tool for exhibitors. However, exhibitors still rely on domain experts who are costly and time consuming because of the manual knowledge input procedure. Moreover, it is prone to biases and errors and not suitable for managing fast-growing and tremendous amounts of data that far exceed a human's ability to comprehend. To overcome these problems, data mining technology may be a great alternative, but it needs to be fit to each exhibition. This study uses data mining technology with the Predictive Model Markup Language (PMML) to suggest a system that supports intelligent services and that improves stakeholder satisfaction. This system provides advantages to the exhibitor, show organizer, and system designer, and is first enhanced by integrating data mining technologies through the knowledge of exhibition experts. Second, using the PMML, the system can automate the process of applying data mining models to solve real-time processing problems in the exhibition environment.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.73-82
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• 2000

This study examined the characteristicso f the knowledge discovery and data mining algorithms to demonstrate how they can be used to predict health outcomes and provide policy information for hypertension management using the Korea Medical Insurance Corporation database. Specifically this study validated the predictive power of data mining algorithms by comparing the performance of logistic regression and two decision tree algorithms CHAID (Chi-squared Automatic Interaction Detection) and C5.0 (a variant of C4.5) since logistic regression has assumed a major position in the healthcare field as a method for predicting or classifying health outcomes based on the specific characteristics of each individual case. This comparison was performed using the test set of 4,588 beneficiaries and the training set of 13,689 beneficiaries that were used to develop the models. On the contrary to the previous study CHAID algorithm performed better than logistic regression in predicting hypertension but C5.0 had the lowest predictive power. In addition CHAID algorithm and association rule also provided the segment characteristics for the risk factors that may be used in developing hypertension management programs. This showed that data mining approach can be a useful analytic tool for predicting and classifying health outcomes data.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.716-718
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• 2004

The false alarm data in intrusion detection systems are divided into false positive and false negative. The false positive makes bad effects on the performance of intrusion detection system. And the false negative makes bad effects on the efficiency of intrusion detection system. Recently, the most of works have been studied the data mining technique for analysis of alert data. However, the false alarm data not only increase data volume but also change patterns of alert data along the time line. Therefore, we need a tool that can analyze patterns that change characteristics when we look for new patterns. In this paper, we focus on the false positives and present a framework for analysis of false alarm pattern from the alert data. In this work, we also apply incremental data mining techniques to analyze patterns of false alarms among alert data that are incremental over the time. Finally, we achieved flexibility by using dynamic support threshold, because the volume of alert data as well as included false alarms increases irregular.

• Journal of Ecology and Environment
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• v.41 no.5
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• pp.142-144
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• 2017

In recent decades, as it becomes increasingly important to monitor and research long-term ecological changes, worldwide attempts are being conducted to integrate and manage ecological data in a unified framework. Especially domestic ecological data in South Korea should be first standardized based on predefined common protocols for data integration, since they are often scattered over many different systems in various forms. Additionally, foreign ecological data should be converted into a proper unified format to be used along with domestic data for association studies. In this study, our interest is to integrate ECN data with Korean domestic ecological data under our unified framework. For this purpose, we employed our semi-automatic data conversion tool to standardize foreign data and utilized ground beetle (Carabidae) datasets collected from 12 different observatory sites of ECN. We believe that our attempt to convert domestic and foreign ecological data into a standardized format in a systematic way will be quite useful for data integration and association analysis in many ecological and environmental studies.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.693-700
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• 2017

The mines in our country are complex in geography and shape and because of its small scale, accurate surveying performance and 3D modeling are necessary for mine development and management and mining plans. However, due to the data acquisition and processing technology and economy, the existing methods are currently used. The structure, mining, and mining area of the mine are recorded and managed based on the 2D drawings. As a result, it is true that there is risk of accidents caused by problems of accuracy as well as waste of personnel and time. In recent years, research data on geology and geospatial information on mines have been integrated into a database in foreign countries, and they are used for mine management and mining planning. In this study, we tried to construct spatial information for mining management and mining plan using laser scanner. Through research, spatial information about the mine was effectively obtained and produced data modeled through data processing. The 3D model for mining mines is expected to be a valuable tool for establishing and operating a safe mining plan for mines.