Recent advances in data mining of enterprise data: algorithms and applications
Gespeichert in:
| Körperschaft: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific
c2007
|
| Schriftenreihe: | Series on computers and operations research
v. 6 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| 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."--P. 785 Includes bibliographical references and index 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 ... [et al.] -- 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 ... [et al.] -- 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 |
| Beschreibung: | 1 Online-Ressource (xxxii, 786 p.) |
| ISBN: | 9789812779861 9812779868 |
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| 500 | |a Includes bibliographical references and index | ||
| 500 | |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 ... [et al.] -- 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 ... [et al.] -- 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 | ||
| 500 | |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 | ||
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| spelling | International Workshop on Mining of Enterprise Data <2004, Como, Italy> Verfasser aut Recent advances in data mining of enterprise data algorithms and applications [editors], T. Warren Liao, Evangelos Triantaphyllou Singapore World Scientific c2007 1 Online-Ressource (xxxii, 786 p.) txt rdacontent c rdamedia 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."--P. 785 Includes bibliographical references and index 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 ... [et al.] -- 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 ... [et al.] -- 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 COMPUTERS / Database Management / Data Mining bisacsh Business enterprises / Data processing fast Data mining fast Datenverarbeitung Unternehmen Data mining Congresses Business enterprises Data processing Congresses (DE-588)1071861417 Konferenzschrift gnd-content Liao, T. Warren Sonstige oth Triantaphyllou, Evangelos Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=236063 Aggregator Volltext |
| spellingShingle | Recent advances in data mining of enterprise data algorithms and applications COMPUTERS / Database Management / Data Mining bisacsh Business enterprises / Data processing fast Data mining fast Datenverarbeitung Unternehmen Data mining Congresses Business enterprises Data processing Congresses |
| subject_GND | (DE-588)1071861417 |
| 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 | COMPUTERS / Database Management / Data Mining bisacsh Business enterprises / Data processing fast Data mining fast Datenverarbeitung Unternehmen Data mining Congresses Business enterprises Data processing Congresses |
| topic_facet | COMPUTERS / Database Management / Data Mining Business enterprises / Data processing Data mining Datenverarbeitung Unternehmen Data mining Congresses Business enterprises Data processing Congresses Konferenzschrift |
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