Recent advances in data mining of enterprise data :: algorithms and applications /
The main goal of the new field of data mining is the analysis of large and complex datasets. Some very important datasets may be derived from business and industrial activities. This kind of data is known as "enterprise data". The common characteristic of such datasets is that the analyst...
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| Format: | Elektronisch Tagungsbericht E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; Hackensack, NJ :
World Scientific,
©2007.
|
| Schriftenreihe: | Series on computers and operations research ;
v. 6. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | The main goal of the new field of data mining is the analysis of large and complex datasets. Some very important datasets may be derived from business and industrial activities. This kind of data is known as "enterprise data". The common characteristic of such datasets is that the analyst wishes to analyze them for the purpose of designing a more cost-effective strategy for optimizing some type of performance measure, such as reducing production time, improving quality, eliminating wastes, or maximizing profit. Data in this category may describe different scheduling scenarios in a manufacturing environment, quality control of some process, fault diagnosis in the operation of a machine or process, risk analysis when issuing credit to applicants, management of supply chains in a manufacturing system, or data for business related decision-making |
| Beschreibung: | " ... the International Workshop on Mining of Enterprise Data, held on June 23, 2004 at Como, Italy, as part of the Mathematics and Machine Learning (MML) Conference. This edited book is a product evolved from this workshop."--Page 785 |
| Beschreibung: | 1 online resource (xxxii, 786 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9789812779861 9812779868 981277985X 9789812779854 |
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Ch. 1. Enterprise data mining: a review and research directions / T.W. Liao -- ch. 2. Application and comparison of classification techniques in controlling credit risk / L. Yu [and others] -- ch. 3. Predictive classification with imbalanced enterprise data / S. Daskalaki, I. Kopanas, and N.M. Avouris -- ch. 4. Using soft computing methods for time series forecasting / P.-C. Chang and Y.-W. Wang -- ch. 5. Data mining applications of process platform formation for high variety production / J. Jiao and L. Zhang -- ch. 6. A data mining approach to production control in dynamic manufacturing systems / H.-S. Min and Y. Yih -- ch. 7. Predicting wine quality from agricultural data with single-objective and multi-objective data mining algorithms / M. Last [and others] -- ch. 8. Enhancing competitive advantages and operational excellence for high-tech industry through data mining and digital management / C.-F. Chien, S.-C. Hsu, and Chia-Yu Hsu -- ch. 9. Multivariate control charts from a data mining perspective / G.C. Porzio and G. Ragozini -- ch. 10. Data mining of multi-dimensional functional data for manufacturing fault diagnosis / M.K. Jeong, S.G. Kong, and O.A. Omitaomu -- ch. 11. Maintenance planning using enterprise data mining / L.P. Khoo, Z.W. Zhong, and H.Y. Lim -- ch. 12. Data mining techniques for improving workflow model / D. Gunopulos and S. Subramaniam -- ch. 13. Mining images of cell-based assays / P. Perner -- ch. 14. Support vector machines and applications / T.B. Trafalis and O.O. Oladunni -- ch. 15. A survey of manifold-based learning methods / X. Huo, X. Ni, and A.K. Smith -- ch. 16. Predictive regression modeling for small enterprise data sets with bootstrap, clustering, and bagging / C.J. Feng and K. Erla. | |
| 520 | |a The main goal of the new field of data mining is the analysis of large and complex datasets. Some very important datasets may be derived from business and industrial activities. This kind of data is known as "enterprise data". The common characteristic of such datasets is that the analyst wishes to analyze them for the purpose of designing a more cost-effective strategy for optimizing some type of performance measure, such as reducing production time, improving quality, eliminating wastes, or maximizing profit. Data in this category may describe different scheduling scenarios in a manufacturing environment, quality control of some process, fault diagnosis in the operation of a machine or process, risk analysis when issuing credit to applicants, management of supply chains in a manufacturing system, or data for business related decision-making | ||
| 650 | 0 | |a Data mining |v Congresses. | |
| 650 | 0 | |a Business enterprises |x Data processing |v Congresses. | |
| 650 | 6 | |a Exploration de données (Informatique) |v Congrès. | |
| 650 | 6 | |a Entreprises |x Informatique |v Congrès. | |
| 650 | 7 | |a COMPUTERS |x Database Management |x Data Mining. |2 bisacsh | |
| 650 | 7 | |a Business enterprises |x Data processing |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
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| 700 | 1 | |a Liao, T. Warren |q (Thunshun Warren), |d 1957- |1 https://id.oclc.org/worldcat/entity/E39PCjC9RQDkqmfG3xyd7xYQMP |0 http://id.loc.gov/authorities/names/no2008083113 | |
| 700 | 1 | |a Triantaphyllou, Evangelos. |0 http://id.loc.gov/authorities/names/n00005470 | |
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| contents | Ch. 1. Enterprise data mining: a review and research directions / T.W. Liao -- ch. 2. Application and comparison of classification techniques in controlling credit risk / L. Yu [and others] -- ch. 3. Predictive classification with imbalanced enterprise data / S. Daskalaki, I. Kopanas, and N.M. Avouris -- ch. 4. Using soft computing methods for time series forecasting / P.-C. Chang and Y.-W. Wang -- ch. 5. Data mining applications of process platform formation for high variety production / J. Jiao and L. Zhang -- ch. 6. A data mining approach to production control in dynamic manufacturing systems / H.-S. Min and Y. Yih -- ch. 7. Predicting wine quality from agricultural data with single-objective and multi-objective data mining algorithms / M. Last [and others] -- ch. 8. Enhancing competitive advantages and operational excellence for high-tech industry through data mining and digital management / C.-F. Chien, S.-C. Hsu, and Chia-Yu Hsu -- ch. 9. Multivariate control charts from a data mining perspective / G.C. Porzio and G. Ragozini -- ch. 10. Data mining of multi-dimensional functional data for manufacturing fault diagnosis / M.K. Jeong, S.G. Kong, and O.A. Omitaomu -- ch. 11. Maintenance planning using enterprise data mining / L.P. Khoo, Z.W. Zhong, and H.Y. Lim -- ch. 12. Data mining techniques for improving workflow model / D. Gunopulos and S. Subramaniam -- ch. 13. Mining images of cell-based assays / P. Perner -- ch. 14. Support vector machines and applications / T.B. Trafalis and O.O. Oladunni -- ch. 15. A survey of manifold-based learning methods / X. Huo, X. Ni, and A.K. Smith -- ch. 16. Predictive regression modeling for small enterprise data sets with bootstrap, clustering, and bagging / C.J. Feng and K. Erla. |
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| discipline | Informatik |
| format | Electronic Conference Proceeding eBook |
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| indexdate | 2024-11-26T14:48:59Z |
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| series | Series on computers and operations research ; |
| series2 | Series on computers and operations research ; |
| spelling | International Workshop on Mining of Enterprise Data (2004 : Como, Italy) http://id.loc.gov/authorities/names/no2009106616 Recent advances in data mining of enterprise data : algorithms and applications / [editors], T. Warren Liao, Evangelos Triantaphyllou. Singapore ; Hackensack, NJ : World Scientific, ©2007. 1 online resource (xxxii, 786 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Series on computers and operations research ; v. 6 " ... the International Workshop on Mining of Enterprise Data, held on June 23, 2004 at Como, Italy, as part of the Mathematics and Machine Learning (MML) Conference. This edited book is a product evolved from this workshop."--Page 785 Includes bibliographical references and index. Print version record. Ch. 1. Enterprise data mining: a review and research directions / T.W. Liao -- ch. 2. Application and comparison of classification techniques in controlling credit risk / L. Yu [and others] -- ch. 3. Predictive classification with imbalanced enterprise data / S. Daskalaki, I. Kopanas, and N.M. Avouris -- ch. 4. Using soft computing methods for time series forecasting / P.-C. Chang and Y.-W. Wang -- ch. 5. Data mining applications of process platform formation for high variety production / J. Jiao and L. Zhang -- ch. 6. A data mining approach to production control in dynamic manufacturing systems / H.-S. Min and Y. Yih -- ch. 7. Predicting wine quality from agricultural data with single-objective and multi-objective data mining algorithms / M. Last [and others] -- ch. 8. Enhancing competitive advantages and operational excellence for high-tech industry through data mining and digital management / C.-F. Chien, S.-C. Hsu, and Chia-Yu Hsu -- ch. 9. Multivariate control charts from a data mining perspective / G.C. Porzio and G. Ragozini -- ch. 10. Data mining of multi-dimensional functional data for manufacturing fault diagnosis / M.K. Jeong, S.G. Kong, and O.A. Omitaomu -- ch. 11. Maintenance planning using enterprise data mining / L.P. Khoo, Z.W. Zhong, and H.Y. Lim -- ch. 12. Data mining techniques for improving workflow model / D. Gunopulos and S. Subramaniam -- ch. 13. Mining images of cell-based assays / P. Perner -- ch. 14. Support vector machines and applications / T.B. Trafalis and O.O. Oladunni -- ch. 15. A survey of manifold-based learning methods / X. Huo, X. Ni, and A.K. Smith -- ch. 16. Predictive regression modeling for small enterprise data sets with bootstrap, clustering, and bagging / C.J. Feng and K. Erla. The main goal of the new field of data mining is the analysis of large and complex datasets. Some very important datasets may be derived from business and industrial activities. This kind of data is known as "enterprise data". The common characteristic of such datasets is that the analyst wishes to analyze them for the purpose of designing a more cost-effective strategy for optimizing some type of performance measure, such as reducing production time, improving quality, eliminating wastes, or maximizing profit. Data in this category may describe different scheduling scenarios in a manufacturing environment, quality control of some process, fault diagnosis in the operation of a machine or process, risk analysis when issuing credit to applicants, management of supply chains in a manufacturing system, or data for business related decision-making Data mining Congresses. Business enterprises Data processing Congresses. Exploration de données (Informatique) Congrès. Entreprises Informatique Congrès. COMPUTERS Database Management Data Mining. bisacsh Business enterprises Data processing fast Data mining fast Conference papers and proceedings fast Liao, T. Warren (Thunshun Warren), 1957- https://id.oclc.org/worldcat/entity/E39PCjC9RQDkqmfG3xyd7xYQMP http://id.loc.gov/authorities/names/no2008083113 Triantaphyllou, Evangelos. http://id.loc.gov/authorities/names/n00005470 Print version: International Workshop on Mining of Enterprise Data (2004 : Como, Italy). Recent advances in data mining of enterprise data. Singapore ; Hackensack, NJ : World Scientific, ©2007 981277985X 9789812779854 (DLC) 2008273546 (OCoLC)191658450 Series on computers and operations research ; v. 6. http://id.loc.gov/authorities/names/no2004018266 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=236063 Volltext |
| spellingShingle | Recent advances in data mining of enterprise data : algorithms and applications / Series on computers and operations research ; Ch. 1. Enterprise data mining: a review and research directions / T.W. Liao -- ch. 2. Application and comparison of classification techniques in controlling credit risk / L. Yu [and others] -- ch. 3. Predictive classification with imbalanced enterprise data / S. Daskalaki, I. Kopanas, and N.M. Avouris -- ch. 4. Using soft computing methods for time series forecasting / P.-C. Chang and Y.-W. Wang -- ch. 5. Data mining applications of process platform formation for high variety production / J. Jiao and L. Zhang -- ch. 6. A data mining approach to production control in dynamic manufacturing systems / H.-S. Min and Y. Yih -- ch. 7. Predicting wine quality from agricultural data with single-objective and multi-objective data mining algorithms / M. Last [and others] -- ch. 8. Enhancing competitive advantages and operational excellence for high-tech industry through data mining and digital management / C.-F. Chien, S.-C. Hsu, and Chia-Yu Hsu -- ch. 9. Multivariate control charts from a data mining perspective / G.C. Porzio and G. Ragozini -- ch. 10. Data mining of multi-dimensional functional data for manufacturing fault diagnosis / M.K. Jeong, S.G. Kong, and O.A. Omitaomu -- ch. 11. Maintenance planning using enterprise data mining / L.P. Khoo, Z.W. Zhong, and H.Y. Lim -- ch. 12. Data mining techniques for improving workflow model / D. Gunopulos and S. Subramaniam -- ch. 13. Mining images of cell-based assays / P. Perner -- ch. 14. Support vector machines and applications / T.B. Trafalis and O.O. Oladunni -- ch. 15. A survey of manifold-based learning methods / X. Huo, X. Ni, and A.K. Smith -- ch. 16. Predictive regression modeling for small enterprise data sets with bootstrap, clustering, and bagging / C.J. Feng and K. Erla. Data mining Congresses. Business enterprises Data processing Congresses. Exploration de données (Informatique) Congrès. Entreprises Informatique Congrès. COMPUTERS Database Management Data Mining. bisacsh Business enterprises Data processing fast Data mining fast |
| title | Recent advances in data mining of enterprise data : algorithms and applications / |
| title_auth | Recent advances in data mining of enterprise data : algorithms and applications / |
| title_exact_search | Recent advances in data mining of enterprise data : algorithms and applications / |
| title_full | Recent advances in data mining of enterprise data : algorithms and applications / [editors], T. Warren Liao, Evangelos Triantaphyllou. |
| title_fullStr | Recent advances in data mining of enterprise data : algorithms and applications / [editors], T. Warren Liao, Evangelos Triantaphyllou. |
| title_full_unstemmed | Recent advances in data mining of enterprise data : algorithms and applications / [editors], T. Warren Liao, Evangelos Triantaphyllou. |
| title_short | Recent advances in data mining of enterprise data : |
| title_sort | recent advances in data mining of enterprise data algorithms and applications |
| title_sub | algorithms and applications / |
| topic | Data mining Congresses. Business enterprises Data processing Congresses. Exploration de données (Informatique) Congrès. Entreprises Informatique Congrès. COMPUTERS Database Management Data Mining. bisacsh Business enterprises Data processing fast Data mining fast |
| topic_facet | Data mining Congresses. Business enterprises Data processing Congresses. Exploration de données (Informatique) Congrès. Entreprises Informatique Congrès. COMPUTERS Database Management Data Mining. Business enterprises Data processing Data mining Conference papers and proceedings |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=236063 |
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