• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.2
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• pp.10-16
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• 2004

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing Process. The Purpose of this Paper is to model the recognition of defect type Patterns and Prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.166-171
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• 2005

With development of the database, there are too many data on process variables and the manufacturing process for the traditional statistical process control methods to identify the process variables related with assignable causes. Data mining is useful in this situation and provides variety of approaches for improving the process. In this paper, we applied control charts to monitor the process and if assignable causes are detected, then we applied the SVM technique and the sequence pattern analysis to find out the process variables suspected. These techniques made possible to predict the behavior of process variables. We illustrated our proposed methods with real manufacturing process data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.118-122
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• 2005

Data mining is the process of autonomously extracting useful information or knowledge from large data stores or sets. For analyzing data of manufacturing processes obtained from database using data mining, source data should be collected form production process and transformed to appropriate form. To extract those data from database, a computer program should be made for each database. This paper presents a program to extract easily data form database in industry. The advantage of this program is that user can extract data from all types of database and database table and interface with Teamcenter Manufacturing.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.4
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• pp.128-136
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• 2005

The use of technology is increasing within the service industry, but there is some doubt as to whether the benefits of employing this technology have been efficiently harnessed such as data mining. Data mining is the process of extracting certain predictive information from databases that can evolve from currently used restaurant management systems. The potential of harnessing this predictive information can have an enormous impact on the restaurant's operation on the whole, particularly in the area customer retention and competition. Since there is insufficient literature on the use of data mining in the restaurant industry, this study is both seminal and investigative, done via a Delphi survey to explore and describe the current and future applications of this process.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.204-209
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• 2002

The interest of data mining in artificial intelligence with fuzzy logic has been increased. Data mining is a process of extracting desirable knowledge and interesting pattern ken large data set. Because of expansion of WWW, web data is more and more huge. Besides mining web contents and web structures, another important task for web mining is web usage mining which mines web log data to discover user access pattern. The goal of web usage mining in this paper is to find interesting user pattern in the web with user feedback. It is very important to find user's characteristic fer e-business environment. In Customer Relationship Management, recommending product and sending e-mail to user by extracted users characteristics are needed. Using our method, we extract user profile from the result of web usage mining. In this research, we concentrate on finding association rules and verify validity of them. The proposed procedure can integrate fuzzy set concept and association rule. Fuzzy association rule uses given server log file and performs several preprocessing tasks. Extracted transaction files are used to find rules by fuzzy web usage mining. To verify the validity of user's feedback, the web log data from our laboratory web server.

• The Journal of Bigdata
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• v.1 no.2
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• pp.65-78
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• 2016

In the rapid change of business environment, it is crucial that several companies with core competence cooperate together in order to deliver competitive products to the market faster. Thus a lot of companies are participating in supply chains and SCM (Supply Chain Management) become more important. To efficiently manage supply chains, the analysis of data from SCM systems is required. In this paper, we explain how to analyze SCM related data with process mining techniques. After discussing the data requirement for process mining, several process mining techniques for the data analysis are explained. To show the applicability of the techniques, we have performed a case study with a company in South Korea. The case study shows that process mining is useful tool to analyze SCM data. On specifically, an overall process, several performance measures, and social networks can be easily discovered and analyzed with the techniques.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.381-389
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• 2022

With the collection of huge volumes of text, image, audio, video or combinations of these, in a word multimedia data, the need to explore them in order to discover possible new, unexpected and possibly valuable information for decision making was born. Starting from the already existing data mining, but not as its extension, multimedia mining appeared as a distinct field with increased complexity and many characteristic aspects. Later, the concept of big data was extended to multimedia, resulting in multimedia big data, which in turn attracted the multimedia big data mining process. This paper aims to survey multimedia data mining, starting from the general concept and following the transition from multimedia data mining to multimedia big data mining, through an up-to-date synthesis of works in the field, which is a novelty, from our best of knowledge.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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• pp.19-25
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• 2005

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.117-124
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• 2001

A data mining system includes various data mining functions such as aggregation, association and classification, among others. To express these data mining function, a powerful data mining query language is needed. In addition, a graphic user interface(GUI) based on the data mining query language is needed for users. In addition, processing a data mining query targeted for a data warehouse, which is the appropriate data repository for decision making, is needed. In this paper, we first build a GUI to enable users to easily define data mining queries. We then propose a data mining query processing framework that can be used to process a data mining query targeted for a data warehouse. We also implement a schema generate a data warehouse schema that is needed to build a data warehouse. Lastly, we show the implementation details of a query processor that can process queries that discover association rules.