Recent Advances in Data Mining of Enterprise Data: Algorithms and ApplicationsWorld Scientific, 2008. gada 15. janv. - 786 lappuses 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 OC enterprise dataOCO. 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. Sample Chapter(s). Foreword (37 KB). Chapter 1: Enterprise Data Mining: A Review and Research Directions (655 KB). Contents: Enterprise Data Mining: A Review and Research Directions (T W Liao); Application and Comparison of Classification Techniques in Controlling Credit Risk (L Yu et al.); Predictive Classification with Imbalanced Enterprise Data (S Daskalaki et al.); Data Mining Applications of Process Platform Formation for High Variety Production (J Jiao & L Zhang); Multivariate Control Charts from a Data Mining Perspective (G C Porzio & G Ragozini); Maintenance Planning Using Enterprise Data Mining (L P Khoo et al.); Mining Images of Cell-Based Assays (P Perner); Support Vector Machines and Applications (T B Trafalis & O O Oladunni); A Survey of Manifold-Based Learning Methods (X Huo et al.); and other papers. Readership: Graduate students in engineering, computer science, and business schools; researchers and practioners of data mining with emphazis of enterprise data mining." |
Saturs
A Review and Research Directions by T W Liao | 1 |
Chapter 2 Application and Comparison of Classification Techniques in Controlling Credit Risk by L Yu G Chen A Koronios S Zhu and X Guo | 111 |
Chapter 3 Predictive Classification with Imbalanced Enterprise Data by S Daskalaki I Kopanas and N M Avouris | 147 |
Chapter 4 Using Soft Computing Methods for Time Series Forecasting by PC Chang and YW Wang | 189 |
Chapter 5 Data Mining Applications of Process Platform Formation for High Variety Production by J Jiao and L Zhang | 247 |
Chapter 6 A Data Mining Approach to Production Control in Dynamic Manufacturing Systems by HS Min and Y Yih | 287 |
Chapter 7 Predicting Wine Quality from Agricultural Data with SingleObjective and MultiObjective Data Mining Algorithms by M Last S Elnekave ... | 323 |
Chapter 8 Enhancing Competitive Advantages and Operational Excellence for HighTech Industry through Data Mining and Digital Management by ... | 367 |
Chapter 11 Maintenance Planning Using Enterprise Data Mining by L P Khoo Z W Zhong and H Y Lim | 505 |
Chapter 12 Data Mining Techniques for Improving Workflow Model by D Gunopulos and S Subramaniam | 545 |
Chapter 13 Mining Images of CellBased Assays by P Perner | 577 |
Chapter 14 Support Vector Machines and Applications by T B Trafalis and O O Oladunni | 643 |
Chapter 15 A Survey of ManifoldBased Learning Methods by X Huo X Ni and A K Smith | 691 |
Chapter 16 Predictive Regression Modeling for Small Enterprise Data Sets with Bootstrap Clustering and Bagging by C J Feng and K Erla | 747 |
| 775 | |
List of Contributors | 779 |
Chapter 9 Multivariate Control Charts from a Data Mining Perspective by G C Porzio and G Ragozini | 413 |
Chapter 10 Data Mining of MultiDimensional Functional Data for Manufacturing Fault Diagnosis by M K Jeong S G Kong and O A Omitaomu | 463 |
About the Editors | 785 |
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Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications Thunshun Warren Liao,Evangelos Triantaphyllou Ierobežota priekšskatīšana - 2008 |
Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications Ierobežota priekšskatīšana |
Bieži izmantoti vārdi un frāzes
analysis applications approach Aroma Score association rules attribute values bootstrap case-based reasoning chapter Chen classification clustering Computer control chart Data Mining Algorithm database dataset decision tree defined detection developed dimension reduction discretization domain Enterprise Data estimate evaluation extracted feature forecasting function fuzzy genetic algorithm Golan Heights Winery Grape Score graph hyperspectral hyperspectral image identify induction input insolvent International knowledge discovery Laplacian eigenmaps layer linear LTSA Machine Learning matrix methodology methods mining and knowledge Mining of Enterprise multivariate neural networks neurons node operation optimal outliers output parameters patterns prediction error principal components problem procedure production proposed regression related data mining rough sets routing sample selection semiconductor manufacturing similarity similarity matrix simulation solution spectral statistical subset support vector machines Table target techniques Trafalis variables wafer lot Wang wavelet weight workflow workflow models