• Korean Journal of Computational Design and Engineering
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• v.17 no.4
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• pp.294-302
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• 2012

Process mining is a useful methodology that can be used for extracting user patterns in log files in order to discover efficient or inefficient processes in organizations. In general, it is used to find and reduce differences between pre-defined processes and actually executed processes in an organization. In this paper, we propose a method to improve processes in PDM/PLM systems based on process mining. In order to improve and detect the inefficient processes, we gathered event logs from PDM/PLM systems and derived process models using several process mining techniques such as ${\alpha}$-algorithm mining, heuristics mining, and fuzzy miner. By comparing original process models with process mining results, it is possible to detect differences between predefined processes and real ones; thereby we can build improved process models for future application.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.210-214
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• 2002

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.399-405
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• 2004

With the explosive growth of information sources available under various information technology and business environment, it has become increasingly necessary for determining effective marketing strategies and optimizing the logical structure of the CRM data mining system. In this paper, we present an overview of the data mining for strategy focused CRM structure. This includes preprocessing, transaction identification and data integration components. We describe the main part of this paper to the discussion of processes and problems that characterize the mining tools and techniques, identify the CRM data mining, and provide a general architecture of a system to do focused CRM data mining that require further research and development.

• The Journal of Information Systems
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• v.14 no.3
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• pp.37-47
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• 2005

Mining of association rules is to find associations among data items that appear together in some transactions or business activities. As of today, algorithms for association rule mining, as well as for other data mining tasks, are mostly applied to relational databases. As XML being adopted as the universal format for data storage and exchange, mining associations from XML data becomes an area of attention for researchers and developers. The challenge is that the semi-structured data format in XML is not directly suitable for traditional data mining algorithms and tools. In this paper we present an encoding method to encode XML tree-nodes. This method is used to store the XML data in Value Table and Transaction Table that can be easily accessed via indexing. The hierarchical relationship in the original XML tree structure is embedded in the encoding. We applied this method to association rules mining of XML data that may have missing data.

• Journal of Intelligence and Information Systems
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• v.4 no.2
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• pp.129-139
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• 1998

통계 software에는 data mining에 필요한 다양한 모형과 함수들이 제공되고 있어 이를 이용한 data mining 도구가 소개되고 있다. 본 논문에서는 data mining을 수행하는데 효과적인 환경을 제공하는 S-Plus로 data mining 기법들을 구현하거나 재구성하였으며, StatServer를 이용하여 대용량의 data base를 직접 관리할 수 있게 하고, S-PLUS의 분석기능을 Internet을 통하여 사용할 수 있게 하여 원거리에서 data mining작업을 수행될 수 있도록 구성하였다. 또한 분석자는 찾아낸 모형을 복잡한 프로그래밍 작업 없이 새로운 웹 페이지를 만들 수 있으며, 이를 통해 운영계의 사용자가 최적 모형이 제시하는 결과를 실제 업무에 즉시 이용할 수 있도록 하였다.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.04a
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• pp.142-148
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• 2006

Data mining is a useful tool for analyzing data from different perspectives and for summarizing them into useful information. Recently, the data mining methods are applied to solving quality problems of the manufacturing processes. This paper discusses the problems of construction of a quality mining system, which is based on the various data mining methods. The quality mining system includes recipe optimization, significant difference test, finding critical processes, forecasting the yield. The contents and system of this paper are focused on the TFT-LCD manufacturing process. We also provide some illustrative field examples of the quality mining system.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.39-48
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• 2020

Data mining techniques have been suggested to find efficiently meaningful and useful information. Especially, in the big data environments, as data becomes accumulated in several applications, related pattern mining methods have been proposed. Recently, instead of analyzing not only static data stored already in files or databases, mining dynamic data incrementally generated in a real time is considered as more interesting research areas because these dynamic data can be only one time read. With this reason, researches of how these dynamic data are mined efficiently have been studied. Moreover, approaches of mining representative patterns such as maximal pattern mining have been proposed since a huge number of result patterns as mining results are generated. As another issue, to discover more meaningful patterns in real world, weights of items in weighted pattern mining have been used, In real situation, profits, costs, and so on of items can be utilized as weights. In this paper, we analyzed weighted maximal pattern mining approaches for data generated incrementally. Maximal representative pattern mining techniques, and incremental pattern mining methods. And then, the application scenarios for analyzing the required commodity patterns in infants are presented by applying weighting representative pattern mining. Furthermore, the performance of state-of-the-art algorithms have been evaluated. As a result, we show that incremental weighted maximal pattern mining technique has better performance than incremental weighted pattern mining and weighted maximal pattern mining.

• Geomechanics and Engineering
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• v.22 no.3
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• pp.255-264
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• 2020

With the ongoing development of deep mining of coal resources, some coal mine dynamic disasters have exhibited characteristics of both coal-gas outbursts and rockbursts. Therefore, research is required on the mechanism of rockburst-outburst coupling disaster. In this study, the failure characteristics of coal-rock combination structures were investigated using lab-scale physical simulation experiments. The energy criterion of the rockburst-outburst coupling disaster was obtained, and the mechanism of the disaster induced by the gas-solid coupling instability of the coal-rock combination structure was determined. The experimental results indicate that the damage of the coal-rock structure is significantly different from that of a coal body. The influence of the coal-rock structure should be considered in the study of rockburst-outburst coupling disaster. The deformation degree of the roof is controlled by the more significant main role of the gas pressure and the difference in the strength between the rock body and the coal body. The outburst holes and spall characteristics of the coal body after the failure of the coal-rock structure are strongly affected by the difference in strength between the roof and the coal body. The research results provide an in-depth understanding of the mechanism of rockburst-outburst coupling disasters in deep mining.