• Title, Summary, Keyword: Datamining

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Datamining: Roadmap to Extract Inference Rules and Design Data Models from Process Data of Industrial Applications

  • Bae Hyeon;Kim Youn-Tae;Kim Sung-Shin;Vachtsevanos George J.
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.5 no.3
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    • pp.200-205
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    • 2005
  • The objectives of this study were to introduce the easiest and most proper applications of datamining in industrial processes. Applying datamining in manufacturing is very different from applying it in marketing. Misapplication of datamining in manufacturing system results in significant problems. Therefore, it is very important to determine the best procedure and technique in advance. In previous studies, related literature has been introduced, but there has not been much description of datamining applications. Research has not often referred to descriptions of particular examples dealing with application problems in manufacturing. In this study, a datamining roadmap was proposed to support datamining applications for industrial processes. The roadmap was classified into three stages, and each stage was categorized into reasonable classes according to the datamining purposed. Each category includes representative techniques for datamining that have been broadly applied over decades. Those techniques differ according to developers and application purposes; however, in this paper, exemplary methods are described. Based on the datamining roadmap, nonexperts can determine procedures and techniques for datamining in their applications.

An application of datamining approach to CQI using the discharge summary (퇴원요약 데이터베이스를 이용한 데이터마이닝 기법의 CQI 활동에의 황용 방안)

  • 선미옥;채영문;이해종;이선희;강성홍;호승희
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • pp.289-299
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    • 2000
  • This study provides an application of datamining approach to CQI(Continuous Quality Improvement) using the discharge summary. First, we found a process variation in hospital infection rate by SPC (Statistical Process Control) technique. Second, importance of factors influencing hospital infection was inferred through the decision tree analysis which is a classification method in data-mining approach. The most important factor was surgery followed by comorbidity and length of operation. Comorbidity was further divided into age and principal diagnosis and the length of operation was further divided into age and chief complaint. 24 rules of hospital infection were generated by the decision tree analysis. Of these, 9 rules with predictive prover greater than 50% were suggested as guidelines for hospital infection control. The optimum range of target group in hospital infection control were Identified through the information gain summary. Association rule, which is another kind of datamining method, was performed to analyze the relationship between principal diagnosis and comorbidity. The confidence score, which measures the decree of association, between urinary tract infection and causal bacillus was the highest, followed by the score between postoperative wound disruption find postoperative wound infection. This study demonstrated how datamining approach could be used to provide information to support prospective surveillance of hospital infection. The datamining technique can also be applied to various areas fur CQI using other hospital databases.

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Datamining technique for successful eCRM, CRM (성공적인 eCRM, CRM을 위한 데이터마이닝 기법)

  • Kang Rae-Goo;Lim Hee-Kyoung;Jung Chai-Yeoung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.9
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    • pp.1596-1601
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    • 2006
  • To customer management finds and forecast customer's various pattern more easily and conveniently rising by important topic that control corporation's success and failure, mmy corporations are introducing CRM and eCRM fast. At past, customer management had been managed by statisticians or special statistics package but it is trend been alternating gradually by datamining technique to do to computerize statistics process based on sudden development of IT. Field that this datamining is used representatively is CRM, eCRM. This paper applied datamining using GA referencing customer data or discount store and sale data or 2004 years. forecasted 2005 years melancholy customer by datamining and proved datamining through comparison with actuality customer data is how effective to eCRM.

A Case Study on segmentation of Department Store using Decision Tree Analysis (의사결정나무 기법을 활용한 백화점의 고객세분화 사례연구)

  • Chae, Kyung-Hee;Kim, Sang-Cheol
    • Journal of Distribution Science
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    • v.8 no.1
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    • pp.13-19
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    • 2010
  • Segmentation, targeting, and positioning are marketing tools used by a company to gain competitive advantage in the market. For an accurate segmentation, various statistics models or datamining techniques are used. Especially, datamining techniques are introduced in the beginning of the 1980s and solved several marketing problems effectively. In this paper, we research about datamining technique for segmentation and analyze customer's transaction data of Department Store using Decision Tree Analysis, one of the dataming technique. After that, we discuss effects and advantages of segmentation using Decision Tree.

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Two-Step Filtering Datamining Method Integrating Case-Based Reasoning and Rule Induction

  • Park, Yoon-Joo;Chol, En-Mi;Park, Soo-Hyun
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • pp.329-337
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    • 2007
  • Case-based reasoning (CBR) methods are applied to various target problems on the supposition that previous cases are sufficiently similar to current target problems, and the results of previous similar cases support the same result consistently. However, these assumptions are not applicable for some target cases. There are some target cases that have no sufficiently similar cases, or if they have, the results of these previous cases are inconsistent. That is, the appropriateness of CBR is different for each target case, even though they are problems in the same domain. Thus, applying CBR to whole datasets in a domain is not reasonable. This paper presents a new hybrid datamining technique called two-step filtering CBR and Rule Induction (TSFCR), which dynamically selects either CBR or RI for each target case, taking into consideration similarities and consistencies of previous cases. We apply this method to three medical diagnosis datasets and one credit analysis dataset in order to demonstrate that TSFCR outperforms the genuine CBR and RI.

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A Trade Strategy in Stock Market using Market Basket Analysis (장바구니분석을 이용한 주식투자전략 수립 방안)

  • 주영진
    • Journal of Information Technology Applications and Management
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    • v.9 no.4
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    • pp.65-78
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    • 2002
  • We propose a new application method of the datamining technique that might help building an efficient trade strategy in the stock market, where the analysis of the huge database is essential. The proposed method utilizes the association rules among the price changes of individual stock from the market basket analysis (a datamining technique typically used in the Marketing field) in building the strategy We also apply the proposed method to the daily stock prices in Korean stock market, from Jan. 2000 to Dec. 2001. The application results show that the proposed method gives an significantly higher yield rate than the actual stock chage rate.

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Practical Utilization of Engineering Data based on Evolutionary Computation Method (진화연산에 의한 공학 데이터의 활용)

  • Lee Kyung-Ho;Yeon Yun-Seog;Yang Young-Soon
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • pp.317-324
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    • 2005
  • Korean shipyards have accumulated a great amount of data. But they do not have appropriate tools to utilize the data in practical works. Engineering data contains experts' experience and know-how In its own. It is very useful to extract knowledge or information from the accumulated existing data by using datamining technique. This paper treats an evolutionary computation method based on genetic programming (GP), which can be one of the components to realize datamining.

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A Comparison of the Performance of Classification for Biomedical Signal using Neural Networks

  • Kim Man-Sun;Lee Sang-Yong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.3
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    • pp.179-183
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    • 2006
  • ECG consists of various waveforms of electric signals of heat. Datamining can be used for analyzing and classifying the waveforms. Conventional studies classifying electrocardiogram have problems like extraction of distorted characteristics, overfitting, etc. This study classifies electrocardiograms by using BP algorithm and SVM to solve the problems. As results, this study finds that SVM provides an effective prohibition of overfitting in neural networks and guarantees a sole global solution, showing excellence in generalization performance.

Development of Datamining Roadmap and Its Application to Water Treatment Plant for Coagulant Control (데이터마이닝 로드맵 개발과 수처리 응집제 제어를 위한 데이터마이닝 적용)

  • Bae, Hyeon;Kim, Sung-Shin;Kim, Ye-Jin
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.9 no.7
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    • pp.1582-1587
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    • 2005
  • In coagulant control of water treatment plants, rule extraction, one of datamining categories, was performed for coagulant control of a water treatment plant. Clustering methods were applied to extract control rules from data. These control rules can be used for fully automation of water treatment plants instead of operator's knowledge for plant control. To perform fuzzy clustering, there are some coefficients to be determined and these kinds of studies have been performed over decades such as clustering indices. In this study, statistical indices were taken to calculate the number of clusters. Simultaneously, seed points were found out based on hierarchical clustering. These statistical approaches give information about features of clusters, so it can reduce computing cost and increase accuracy of clustering. The proposed algorithm can play an important role in datamining and knowledge discovery.