Pregled bibliografske jedinice broj: 226396
Combining Neural Networks and On-line Analytical Processing in Sale Prediction of Footware and Leather Goods
Combining Neural Networks and On-line Analytical Processing in Sale Prediction of Footware and Leather Goods // Proceedings of 10th International Conference on Operational Research KOI 2004 / Scitovski, Rudolf ; Jukić, Dragan (ur.).
Trogir: University of Osijek, Department of Mathematics, 2004. str. 131-143 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 226396 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Combining Neural Networks and On-line Analytical Processing in Sale Prediction of Footware and Leather Goods
Autori
Zekić-Sušac, Marijana ; Kolarić, Damir ; Piasevoli, Tomislav
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of 10th International Conference on Operational Research KOI 2004
/ Scitovski, Rudolf ; Jukić, Dragan - Trogir : University of Osijek, Department of Mathematics, 2004, 131-143
Skup
International Conference on Operational Research
Mjesto i datum
Trogir, Hrvatska, 22.09.2004. - 24.09.2004
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
data mining; data warehousing; footwear and leather goods; neural networks; OLAP
Sažetak
Methodological basis of a data warehouse system consists of classical statistical methods used with on-line analytical processing (OLAP) technology at the lower level, enhanced with more sophisticated data mining techniques at the higher level. The paper shows a way of integrating the two levels such that drill-up, drill-down, and drill-through results are used as the additional input in modeling neural networks. The research is performed on a real dataset collected from a Croatian footwear and leather goods company that deals with the problem of allocating their goods among a number of retail stores. Prediction of weekly sale for each of the stores is important for planning inventory and satisfying customer needs. OLAP drilling methods are used to reveal some key issues in previous sale on the first level of experiment. The results of these analyses are used to create new input variables included in the neural network model for sale prediction on the second level. The paper compares the results of the neural network model that use such additional knowledge with the model that use only raw data. Guidelines for future research regarding modeling in data mining systems are also discussed.
Izvorni jezik
Engleski
Znanstvena područja
Informacijske i komunikacijske znanosti
POVEZANOST RADA
