Recent Advances In Data Mining Of Enterprise Data: Algorithms And Applications
Algorithms and applications
Published on 16. January 2008
Book
Hardback
820 pages
978-981-277-985-4 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Singapore
Singapore
Target group
College/higher education
Graduate students in engineering, computer science, and business schools, researchers and practioners of data mining with emphazis of enterprise data mining.
Product notice
sewn/stitched
Paper over boards
Dimensions
Height: 229 mm
Width: 157 mm
Thickness: 50 mm
Weight
1247 gr
ISBN-13
978-981-277-985-4 (9789812779854)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Content
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.