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49th CIRP Conference on Manufacturing Systems (CIRP-CMS)

作者： Huber, J. Tammer, C. Krotil, S. Waidmann, S. Hao, X. Seidel, C. Reinhart, G. Fraunhofer IWU Project Grp Resource Efficient Mec Beim Glaspalast 5 D-86153 Augsburg Germany

The growing demand and new fields of application compel battery manufacturers to higher product quality. Thus, defect-free battery separators are a prerequisite for safe lithium-ion cells. Hence, typical production faults have to be reliably detected. Therefore, a machine vision system was developed [1]. Besides the detection of anomalies, a key element is the distinction between defect classes in order to distinguish non quality related optical effects from faults using a machine learning approach for classification. Therefore, a method consisting of five phases is described. Starting with data analysis and preparation, expert knowledge is used to derive a database. On this basis models are generated by machine learning algorithms. The best model is chosen by a weighted evaluation of the target values using a preference matrix. Furthermore, the model gets evaluated by an analysis of the confusion matrix. Finally the implementation phase serves to transfer the results into the machine vision system and to monitor occurring process drifts. Last, the functionality of the method is proven within an application example. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

关键词： Optical inspection battery separator classification machine learning data mining knowledge discovery in databases decision trees

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Analyzing unstructured Facebook social network data through web text mining: A study of online shopping firms in Turkey

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INFORMATION DEVELOPMENT 2016年第1期32卷 70-80页

作者： Kahya-Ozyirmidokuz, Esra Erciyes Univ Fac Econ & Adm Sci Kayseri Turkey

The large amounts of Facebook social network data which are generated and collected need to be analyzed for valuable decision making information about shopping firms in Turkey. In addition, analyzing social network data from outside the firms becomes a critical business need for the firms which actively use Facebook. To have a competitive advantage, firms must translate social media texts into something more quantitative to extract information. In this study, web text mining techniques are used to determine popular online shopping firms' Facebook patterns. For this purpose, 200 popular Turkish companies' web URLs are used. Web text mining through natural language processing techniques is examined. Similarity analysis and clustering are done. Consequently, the clusters of the Facebook websites and their relationships and similarities of the firms are obtained.

关键词： text mining web mining Facebook knowledge discovery in databases data mining information extraction online shopping Turkey

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Mission Dependency Index of Air Force Built Infrastructure: knowledge discovery with Machine Learning

Mission Dependency Index of Air Force Built Infrastructure: ...

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作者： Clark W. Smith Air Force Institute of Technology

学位级别：硕士

Mission Dependency Index (MDI) is a metric developed to capture the relative criticality of infrastructure assets with respect to organizational missions. The USAF adapted the MDI metric from the United States Navy's MDI methodology. Unlike the Navy's MDI data collection process, the USAF adaptation of the MDI metric employs generic facility category codes (CATCODEs) to assign MDI values. This practice introduces uncertainty into the MDI assignment process with respect to specific missions and specific infrastructure assets. The uncertainty associated with USAF MDI values necessitated the MDI adjudication process. The MDI adjudication process provides a mechanism for installation civil engineer personnel to lobby for accurate MDI values for specific infrastructure assets. The MDI adjudication process requires manual review of facilities and MDI discrepancies, justification documentation, and extensive coordination between organizations. In light of the existing uncertainty with USAF MDI values and the level of effort required for the MDI adjudication process, this research pursues machine learning and the knowledge discovery in databases (KDD) process to identify and understand relationships between real property data and mission critical infrastructure. Furthermore, a decision support tool is developed for the MDI adjudication process. Specifically, supervised learning techniques are employed to develop a classifier that can identify potential MDI discrepancies. This automation effort serves to minimize the manual MDI review process by identifying a subset of facilities for review and potential adjudication.

关键词： Mission Dependency Infrastructure Asset Management Machine Learning knowledge discovery in databases

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Method for Classification of Battery Separator Defects Using Optical Inspection

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Procedia CIRP 2016年 57卷 585-590页

作者： J. Huber C. Tammer S. Krotil S. Waidmann X. Hao C. Seidel G. Reinhart Fraunhofer IWU Project Group Resource-efficient Mechatronic Processing Machines Beim Glaspalast 5 86153 Augsburg Germany

The growing demand and new fields of application compel battery manufacturers to higher product quality. Thus, defect-free battery separators are a prerequisite for safe lithium-ion cells. Hence, typical production faults have to be reliably detected. Therefore, a machine vision system was developed [1] . Besides the detection of anomalies, a key element is the distinction between defect classes in order to distinguish non-quality related optical effects from faults using a machine learning approach for classification. Therefore, a method consisting of five phases is described. Starting with data analysis and preparation, expert knowledge is used to derive a database. On this basis models are generated by machine learning algorithms. The best model is chosen by a weighted evaluation of the target values using a preference matrix. Furthermore, the model gets evaluated by an analysis of the confusion matrix. Finally the implementation phase serves to transfer the results into the machine vision system and to monitor occurring process drifts. Last, the functionality of the method is proven within an application example.

关键词： Optical inspection battery separator classification machine learning data mining knowledge discovery in databases decision trees

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First Steps Towards Curriculum Development Decisions From Raw Educational Data

First Steps Towards Curriculum Development Decisions From Ra...

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International Conference on Development and Application Systems

作者： Mirela Danubianu Faculty of Electrical Engineering and Computer Science "Stefan cel Mare " University Integrated Center for research development and innovation in Advanced Materials Nanotechnologies and Distributed Systems for fabrication and control Suceava Romania

ISBN: (纸本)9781509019946

The massive use of Information and Communication Technology in education allowed to collect and store a huge amount of various data about all educational aspects. The analysis of these raw data could lead to new, unexpected but valuable knowledge, useful for both teachers and students, and also for faculties and universities managers. In this paper a knowledge discovery in databases process, applied on data collected mainly from a Learning Management System implemented in "Stefan cel Mare" University of Suceava is presented.

关键词： knowledge discovery in databases CRISP-DM model data mining classification association rules

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Identifying nonconformity root causes using applied knowledge discovery

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2015年 36卷 84-92页

作者： Donauer, Michael Pecas, Paulo Azevedo, Americo INESC TEC P-4200465 Oporto Portugal Univ Porto Fac Engn P-4200465 Oporto Portugal Univ Tecn Lisboa Inst Super Tecn IDMEC P-1049001 Lisbon Portugal

Quality control, failure analysis and improvement are central elements in manufacturing. Total Quality Management (TQM) provides several quality oriented tools and techniques which, in the event of things, are not always applicable. The increased use of Information Technology (IT) in manufacturing means increased data availability and improved potential for knowledge extraction. Exploiting this knowledge requires data storage and processing facilities with demanding, time consuming sessions for interpretation. Without suitable tools and techniques, knowledge remains hidden in databases. This paper presents a method to help identify root causes of nonconformities (NCs) using a pattern identification approach. Hereby, a general framework, knowledge discovery in databases (KDD), is adapted. This adaptation involves incorporating an economic concentration measure, the Herfindahl-Hirschman Index (HHI), as the data mining algorithm. After presenting the theoretical background, a new methodology is proposed. The suggested approach can be regarded as a quality tool to help make root cause identification of failures simpler and more agile. A case study from the automotive industry is examined using this tool. Results are obtained and presented in the form of matrix based patterns. They suggest that concentration indices help indicate possible root causes of NCs, warranting further investigation in this area. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Total Quality Management Nonconformities Root cause analysis Quality tools knowledge discovery in databases Herfindahl-Hirschman Index

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Efficient discovery of correlated patterns using multiple minimum all-confidence thresholds

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2015年第3期45卷 357-377页

作者： Rage, Uday Kiran Kitsuregawa, Masaru Univ Tokyo Inst Ind Sci Tokyo Japan Natl Inst Informat Tokyo Japan

Correlated patterns are an important class of regularities that exist in a database. Although there exists no universally acceptable best measure to judge the interestingness of a pattern, all-confidence is emerging as a popular measure to discover the patterns. It is because the measure satisfies both the anti-monotonic and null-invariance properties. The former property makes the pattern mining practicable in real-world applications. The latter property facilitates the user to discover the patterns involving both frequent and rare items without generating the huge number of patterns. In this paper, we show that though the measure satisfies the null-invariance property, mining the patterns containing both frequent and rare items with a single minimum all-confidence (minAllConf) threshold leads to the dilemma known as "rare item problem." At a high minAllConf, the discovered correlated patterns involving rare items have very short length. At a low minAllConf, combinatorial explosion can occur, producing too many patterns. To confront the problem, the paper introduces an alternative model based on the concept of multiple minAllConf thresholds. The proposed model generalizes the existing model of correlated patterns and facilitates the user to specify a different minAllConf for each pattern depending upon its items' frequencies. A pattern-growth algorithm, called GCoMine, has also been proposed to discover the patterns. Experiment results show that GCoMine is efficient, and the proposed model can address the problem effectively.

关键词： Data mining knowledge discovery in databases Correlated patterns Rare item problem

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Progress in Biomedical knowledge discovery: A 25-year Retrospective

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Yearbook of medical informatics 2016年第S 01期Suppl 1卷 S117-S129页

作者： Sacchi, L. Holmes, J.H. John H Holmes Institute for Biomedical Informatics University of Pennsylvania School of Medicine 717 Blockley Hall 423 Guardian Drive Philadelphia Fax: 215-573-5325 PA United States

OBJECTIVES: We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning.;METHODS: We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: knowledge discovery OR Data Mining OR Pattern discovery OR Pattern Recognition, Automated.;RESULTS: A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992- 2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992.;CONCLUSIONS: Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains. Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25

关键词： Algorithms artificial intelligence data mining databases factual knowledge discovery in databases

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MOGACAR: A Method for Filtering Interesting Classification Association Rules 11th

MOGACAR: A Method for Filtering Interesting Classification A...

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11th International Conference on Machine Learning and Data Mining (MLDM)

作者： Prado, Diana Benavides Univ Los Andes Syst & Comp Engn Dept Bogota Colombia

ISBN: (纸本)9783319210247;9783319210230

knowledge discovery process is intended to provide valid, novel, potentially useful and finally understandable patterns from data. An interesting research area concerns the identification and use of interestingness measures, in order to rank or filter results and provide what might be called better knowledge. For association rules mining, some research has been focused on how to filter itemsets and rules, in order to guide knowledge acquisition from the user's point of view, as well as to improve efficiency of the process. In this paper, we explain MOGACAR, an approach for ranking and filtering association rules when there are multiple technical and business interestingness measures;MOGACAR uses a multi-objective optimization method based on genetic algorithm for classification association rules, with the intention to find the most interesting, and still valid, itemsets and rules.

关键词： knowledge discovery in databases Data mining Interestingness measures Genetic algorithms Classification association rules

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Automatic Information Retrieval and Preprocessing for Energy Management 1st

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1st Conference on Creativity in Intelligent Technologies and Data Science (CIT and DS)

作者： Sokolov, Alexander Tyukov, Anton Sadovnikova, Natalya Zhuk, Sergey Khrzhanovskaya, Olga Brebels, Adriaan Volgograd State Tech Univ Volgograd Russia Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9783319237664;9783319237657

Authors present a solution for seamless real time integration of different heterogeneous data sources into energy management mashups. Seamless integration means automation of the following stages in knowledge discovery in databases (KDD) process: data selection, preprocessing, transformation, and export. An application selects data from (1) controllers with sensors, (2) HTML pages with weather data and (3) WCF or JSON based interfaces. After selection, data is being preprocessed and transformed into certain predefined format and integrated with other data from other sources. After preprocessing it is delivered to data sink, which can be a database or saved into file for Weka, R or Octave. User uses web-interface for management of data sources, preprocessors, setting up schedule, data format and destination of data export. Proposed solution reduces time of data analysis and creation of energy management web-mashups.

关键词： lambda architecture speed layer knowledge discovery in databases mash-up Internet of things data integration energy management BEMS

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