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001 ocn261350150
003 OCoLC
005 20160527041121.3
006 m o d
007 cr cnu---unuuu
008 081009s2008 si a ob 101 0 eng d
019 696629503
020 9789812779861|q(electronic book)
020 9812779868|q(electronic book)
020 981277985X
020 9789812779854
035 (OCoLC)261350150|z(OCoLC)696629503
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049 RIDW
050 4 QA76.9.D343|bI589 2004eb
072 7 COM|x021030|2bisacsh
082 04 006.312|222
090 QA76.9.D343|bI589 2004eb
111 2 International Workshop on Mining of Enterprise Data|d(2004
:|cComo, Italy)|0https://id.loc.gov/authorities/names/
no2009106616
245 10 Recent advances in data mining of enterprise data :
|balgorithms and applications /|c[editors], T. Warren Liao,
Evangelos Triantaphyllou.
264 1 Singapore ;|aHackensack, NJ :|bWorld Scientific,|c[2007]
264 4 |c©2007
300 1 online resource (xxxii, 786 pages) :|billustrations.
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
340 |gpolychrome|2rdacc
347 text file|2rdaft
490 1 Series on computers and operations research ;|vv. 6
500 " ... 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.
504 Includes bibliographical references and index.
505 0 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 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.
588 0 Print version record.
590 eBooks on EBSCOhost|bEBSCO eBook Subscription Academic
Collection - North America
650 0 Data mining|vCongresses.|0https://id.loc.gov/authorities/
subjects/sh2008102035
650 0 Business enterprises|xData processing|0https://id.loc.gov/
authorities/subjects/sh2009117980|vCongresses.|0https://
id.loc.gov/authorities/subjects/sh99001533
650 7 Data mining.|2fast|0https://id.worldcat.org/fast/887946
650 7 Business enterprises|xData processing.|2fast|0https://
id.worldcat.org/fast/842543
655 4 Electronic books.
655 7 Conference papers and proceedings.|2fast|0https://
id.worldcat.org/fast/1423772
655 7 Conference papers and proceedings.|2lcgft|0https://
id.loc.gov/authorities/genreForms/gf2014026068
700 1 Liao, T. Warren|q(Thunshun Warren),|d1957-|0https://
id.loc.gov/authorities/names/no2008083113
700 1 Triantaphyllou, Evangelos.|0https://id.loc.gov/authorities
/names/n00005470
776 08 |iPrint version:|aInternational Workshop on Mining of
Enterprise Data (2004 : Como, Italy).|tRecent advances in
data mining of enterprise data.|dSingapore ; Hackensack,
NJ : World Scientific, ©2007|z981277985X|z9789812779854
|w(DLC) 2008273546|w(OCoLC)191658450
830 0 Series on computers and operations research ;|0https://
id.loc.gov/authorities/names/no2004018266|vv. 6.
856 40 |uhttps://rider.idm.oclc.org/login?url=http://
search.ebscohost.com/login.aspx?direct=true&scope=site&
db=nlebk&AN=236063|zOnline eBook. Access restricted to
current Rider University students, faculty, and staff.
856 42 |3Instructions for reading/downloading this eBook|uhttp://
guides.rider.edu/ebooks/ebsco
901 MARCIVE 20231220
948 |d201606016|cEBSCO|tebscoebooksacademic|lridw
994 92|bRID
