Pregled bibliografske jedinice broj: 763107
Outlier Detection as the Primary Step for Promotion Planning in Retail
Outlier Detection as the Primary Step for Promotion Planning in Retail // MIPRO 2015, 38th International Convention, May 25 - 29, 2015, Opatija, Croatia, Proceedings / Biljanović, Petar (ur.).
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2015. str. 1681-1686 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Outlier Detection as the Primary Step for Promotion Planning in Retail
Autori
Banek, Marko ; Osrečki, Dinko ; Vranić, Mihaela ; Pintar, Damir
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
MIPRO 2015, 38th International Convention, May 25 - 29, 2015, Opatija, Croatia, Proceedings
/ Biljanović, Petar - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2015, 1681-1686
ISBN
978-953-233-083-0
Skup
MiproBIS, International Conference on Business Intelligence Systems, in conjunction with MIPRO 2015, 38th International Convention
Mjesto i datum
Opatija, Hrvatska, 25.05.2015. - 29.05.2015
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
outlier; outlier detection; multivariate outliers; multidimensional data analysis
Sažetak
Forecasting the increase of customer demand during discount promotions is a fundamental business assignment in retail, which can nowadays be performed by sophisticated data mining algorithms. The calculations are based on data gained during previous promotions. Apart from choosing the right mining algorithm, the quality of prediction models strongly depends on the quality of the training data. Outliers are points of data that do not conform to a defined notion of normal behavior and are therefore either excluded from the training set, or their impact on the model is weighted in a manner different from other data. In this paper we propose a new approach to outlier analysis, with the aim of distinguishing between the outliers associated with an outlier-generating store or product and the outliers that can be classified as noisy data. Outlier analysis is performed with a multidimensional perception of the dataset typical for data warehousing and OLAP. We also introduce measures that estimate the probability of store or product being an outlier generator and conduct experiments to determine their critical threshold values.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb