Revealing sales trends through data mining

Plessas-Leonidis, S; Leopoulos, V; Kirytopoulos, K

dc.contributor.author	Plessas-Leonidis, S	en
dc.contributor.author	Leopoulos, V	en
dc.contributor.author	Kirytopoulos, K	en
dc.date.accessioned	2014-03-01T02:46:58Z
dc.date.available	2014-03-01T02:46:58Z
dc.date.issued	2010	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32959
dc.subject	Case study	en
dc.subject	Data mining	en
dc.subject	Sales forecasting	en
dc.subject.other	Data mining techniques	en
dc.subject.other	Publishing industry	en
dc.subject.other	Sales forecasting	en
dc.subject.other	Forecasting	en
dc.subject.other	Publishing	en
dc.subject.other	Research	en
dc.subject.other	Sales	en
dc.subject.other	Data mining	en
dc.title	Revealing sales trends through data mining	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICCAE.2010.5451296	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICCAE.2010.5451296	en
heal.identifier.secondary	5451296	en
heal.publicationDate	2010	en
heal.abstract	Data mining is defined as the process of discovering patterns in data. This paper presents a case study of the implementation of data mining techniques in revealing sales trends within the publishing industry. Conclusions of this research, based on successful and unsuccessful trials, indicate that data mining is indeed a valuable tool, however, selection of the appropriate variables - data to be used in relevant models is the only parameter that can define the predictive success or not of the algorithms. ©2010 IEEE.	en
heal.journalName	2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010	en
dc.identifier.doi	10.1109/ICCAE.2010.5451296	en
dc.identifier.volume	1	en
dc.identifier.spage	682	en
dc.identifier.epage	687	en