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Data mining for customer service support

INFORMATION & MANAGEMENT 2000年第1期38卷 1-13页

作者： Hui, SC Jha, G Nanyang Technol Univ Sch Appl Sci Singapore 639798 Singapore

In traditional customer service support of a manufacturing environment, a customer service database usually stores two types of service information: (1) unstructured customer service reports record machine problems and its remedial actions and (2) structured data on sales, employees, and customers for day-to-day management operations. This paper investigates how to apply data mining techniques to extract knowledge from the database to support two kinds of customer service activities: decision support and machine fault diagnosis. A data mining process, based on the data mining tool DBMiner, was investigated to provide structured management data for decision support. In addition, a data mining technique that integrates neural network, case-based reasoning, and rule-based reasoning is proposed;it would search the unstructured customer service records for machine fault diagnosis. The proposed technique has been implemented to support intelligent fault diagnosis over the World Wide Web. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： data mining knowledge discovery in databases customer service support decision support machine fault diagnosis

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Interactive visual exploration of association rules with rule-focusing methodology

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knowledge AND INFORMATION SYSTEMS 2007年第1期13卷 43-75页

作者： Blanchard, Julien Guillet, Fabrice Briand, Henri Ecole Polytech Univ Nantes Dept Informat F-44306 Nantes France

On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge for decision making, the user (a decision maker specialized in the data studied) needs to rummage through the rules. To assist him/her in this task, we here propose the rule-focusing methodology, an interactive methodology for the visual post-processing of association rules. It allows the user to explore large sets of rules freely by focusing his/her attention on limited subsets. This new approach relies on rule interestingness measures, on a visual representation, and on interactive navigation among the rules. We have implemented the rule-focusing methodology in a prototype system called ARVis. It exploits the user's focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm.

关键词： knowledge discovery in databases association rules post-processing interactive visualization rule focusing constraint-based mining interestingness measures neighborhood of rules

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Reducing dimensionality in a database of sleep EEG arousals

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第6期38卷 7746-7754页

作者： Alvarez-Estevez, Diego Sanchez-Marono, Noelia Alonso-Betanzos, Amparo Moret-Bonillo, Vicente Univ A Coruna Dept Comp Sci Lab Res & Dev Artificial Intelligence LIDIA La Coruna 15071 Spain

Sleep studies are carried out in order to diagnose those diseases associated with the sleep. The standard technique consists on monitoring various bio-physiological signals of the patient during sleep. The resulting recording, the polysomnography (PSG) is then analyzed offline by the physician. This supposes a very time-consuming task and therefore automation of these analyses is desirable. An arousal during sleep is defined as an abrupt shift in EEG frequency. Normal structure of sleep is altered by the presence of these events, thus being an important factor that influences on the quality of sleep. The use of computing assistance for the detection of these events on the PSG is aimed at reducing the cost of the PSG test, both in economical and human resources. In this work, a dataset containing PSGs of real patients was used for the detection of arousals in sleep. A total of 42 features were extracted from biosignals for the detection of these events. Our aim was to use different feature selection methods to eliminate the redundant features studying their influence on the identification of sleep arousals, checking whether classification could be improved. The objective is to reduce the number of features, identifying the subset of those with more relevance while preserving a good performance on the classifier. Two approximations are explored, wrappers and filters, using different methods of both, and also combinations of each of the methods by means of the union and the intersection of the relevant features obtained. The results showed that discarding the irrelevant features by these methods is feasible, reducing the dimensionality on the input space and also improving the accuracy of the classifiers. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Feature selection knowledge discovery in databases Machine learning Sleep studies

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A knowledge discovery framework for civil infrastructure: A case study of the intelligent workplace

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ENGINEERING WITH COMPUTERS 2000年第3-4期16卷 264-274页

作者： Buchheit, RB Garrett, JH Lee, SR Brahme, R Carnegie Mellon Univ Dept Civil & Environm Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Architecture Pittsburgh PA 15213 USA

Large databases are becoming increasingly common in civil infrastructure applications. Although it is relatively simple to specifically query these databases at a low level, more abstract questions like 'How does the environment affect pavement cracking?' are difficult to answer with traditional methods. Data mining techniques can provide a solution for learning abstract knowledge from civil infrastructure databases. However, data mining needs to be performed within a systematic process to ensure correct and reproducible results. Many decisions must be made during this process, making it difficult for novice analysts to apply. data mining techniques thoroughly. This paper presents an application of a knowledge discovery process to data collected for an 'intelligent' building. The knowledge discovery process is illustrated and explained through this case study. Additionally, we discuss the importance of this case study in the context of a research effort to develop an interactive guide for the knowledge discovery process.

关键词： data mining intelligent buildings knowledge discovery in databases

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Product design and manufacturing process improvement using association rules

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2006年第2期220卷 243-254页

作者： Shahbaz, M. Srinivas Harding, J. A. Turner, M. Univ Loughborough Wolfson Sch Mech & Mfg Engn Loughborough LE11 3TU Leics England Rolls Royce PLC Barnoldswick Yorks England

Modern manufacturing systems equipped with computerized data logging systems collect large volumes of data in real time. The data may contain valuable information for operation and control strategies as well as providing knowledge of normal and abnormal operational patterns. knowledge discovery in databases can be applied to these data to unearth hidden, unknown, representable, and ultimately useful knowledge. Data mining offers tools for discovery of patterns, associations, changes, anomalies, rules, and statistically significant structures and events in data. Extraction of previously unknown, meaningful information from manufacturing databases provides knowledge that may benefit many application areas within the enterprise, for example improving design or fine tuning production processes. This paper examines the application of association rules to manufacturing databases to extract useful information about a manufacturing system's capabilities and its constraints. The quality of each identified rule is tested and, from numerous rules, only those that are statistically very strong and contain substantial design information are selected. The final set of extracted rules contains very interesting information relating to the geometry of the product and also indicates where limitations exist for improvement of the manufacturing processes involved in the production of complex geometric shapes.

关键词： knowledge discovery in databases data mining manufacturing association rules product data product quality and quality control

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Association rules mining using heavy itemsets

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DATA & knowledge ENGINEERING 2007年第1期61卷 93-113页

作者： Palshikar, Girish K. Kale, Mandar S. Apte, Manoj M. Tata Res Dev & Design Ctr Pune 411013 Maharashtra India Tata Consultancy Serv Ltd Engn & Ind Serv Pune 411001 Maharashtra India

A well-known problem that limits the practical usage of association rule mining algorithms is the extremely large number of rules generated. Such a large number of rules makes the algorithms inefficient and makes it difficult for the end users to comprehend the discovered rules. We present the concept of a heavy itemset. An itemset A is heavy (for given support and confidence values) if all possible association rules made up of items only in A are present. We prove a simple necessary and sufficient condition for an itemset to be heavy. We present a formula for the number of possible rules for a given heavy itemset, and show that a heavy itemset compactly represents an exponential number of association rules. Along with two simple search algorithms, we present an efficient greedy algorithm to generate a collection of disjoint heavy itemsets in a given transaction database. We then present a modified apriori algorithm that starts with a given collection of disjoint heavy itemsets and discovers more heavy itemsets, not necessarily disjoint with the given ones. (c) 2006 Elsevier B.V. All rights reserved.

关键词： association rules data mining knowledge discovery in databases knowledge compression

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A review of data mining applications for quality improvement in manufacturing industry

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第10期38卷 13448-13467页

作者： Koksal, Gulser Batmaz, Inci Testik, Murat Caner Middle E Tech Univ Dept Ind Engn TR-06531 Ankara Turkey Middle E Tech Univ Dept Stat TR-06531 Ankara Turkey Hacettepe Univ Dept Ind Engn TR-06800 Ankara Turkey

Many quality improvement (QI) programs including six sigma, design for six sigma, and kaizen require collection and analysis of data to solve quality problems. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for QI in manufacturing. Although a few review papers have recently been published to discuss DM applications in manufacturing, these only cover a small portion of the applications for specific QI problems (quality tasks). In this study, an extensive review covering the literature from 1997 to 2007 and several analyses on selected quality tasks are provided on DM applications in the manufacturing industry. The quality tasks considered are;product/process quality description, predicting quality, classification of quality, and parameter optimisation. The review provides a comprehensive analysis of the literature from various points of view: data handling practices, DM applications for each quality task and for each manufacturing industry, patterns in the use of DM methods, application results, and software used in the applications are analysed. Several summary tables and figures are also provided along with the discussion of the analyses and results. Finally, conclusions and future research directions are presented. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： knowledge discovery in databases Data mining Quality improvement Six sigma Design for six sigma Quality description Prediction Classification Parameter optimisation Data mining software Manufacturing

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A 2D-3D visualization support for human-centered rule mining

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COMPUTERS & GRAPHICS-UK 2007年第3期31卷 350-360页

作者： Blanchard, Julien Pinaud, Bruno Kuntz, Pascale Guillet, Fabrice Univ Nantes Polytech Sch LINA FRE CNRS 2729 KnOwledge & Decis Team F-44035 Nantes France

On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge, the user needs to rummage through the rules. To make this task easier, we propose a new interactive mining methodology based on well adapted dynamic visual representations. It allows the user to drive the discovery process by focusing his/her attention on limited subsets of rules. We have implemented our methodology with two complementary 2D and 3D visualization supports. These implementations exploit the user's focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： knowledge discovery in databases association rules interactive visualization interactive mining rule focusing

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An Overview of Data Mining and knowledge discovery

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Journal of Computer Science & Technology 1998年第4期13卷 348-368页

作者：范建华李德毅 DepartmentofComputerScience CommunicationEngineeringInstituteNanjing210016PR.China DepartmentofComp

With massive amounts of data stored in databases, mining information and knowledge in databases has become an important issue in recent research. Researchers in many different fields have shown great interest in data mining and knowledge discovery in databases. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining and knowledge discovery techniques to understand user behavior better, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a comprehensive survey on the data mining and knowledge discovery techniques developed recently, and introduce some real application systems as well. In conclusion, this article also lists some problems and challenges for further research.

关键词： knowledge discovery in databases data mining machine learning,association rule classification data clustering data generalization pattern searching

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A predictive association rule-based maintenance policy to minimize the probability of breakages: application to an oil refinery

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第9期105卷 3661-3675页

作者： Antomarioni, Sara Pisacane, Ornella Potena, Domenico Bevilacqua, Maurizio Ciarapica, Filippo Emanuele Diamantini, Claudia Univ Politecn Marche Dipartimento Ingn Ind & Sci Matemat Via B Bianche 12 I-60131 Ancona Italy Univ Politecn Marche Dipartimento Ingn Informaz Via B Bianche 12 I-60131 Ancona Italy

Effective maintenance policies can support companies to deal with process interruptions and consequently, to prevent significant profit losses. Moreover, the proliferation of structured and unstructured data due to production plants validates the application of knowledge discovery in databases techniques to increase processes' reliability. In this paper, an innovative maintenance policy is proposed. It aims at both predicting components breakages through association rule mining and determining the optimal set of components to repair in order to improve the overall plant's reliability, under time and budget constraints. An experimental campaign is carried out on a real-life case study concerning an oil refinery plant. Finally, numerical results are discussed considering different blockage categories and number of components and by varying some significant input parameters.

关键词： Predictive maintenance Association rules Integer linear programming knowledge discovery in databases Industry 4 0

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