Tehnički vjesnik, Vol. 24 No. 6, 2017.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20151008153755
Self-organizing maps with sliding window (SOM+SW)
Ulaş Çelenk
; Istanbul University, INNOVA, ITU Ayazaga Campus Teknokent ARI-4, Maslak, Istanbul, Turkey
Duygu Çelik Ertuğrul
orcid.org/0000-0003-1380-705X
; Eastern Mediterranean University, Engineering Faculty, Computer Engineering Department, Famagusta, North Cyprus, via Mersin -10, Turkey
Metin Zontul
; Istanbul Aydin University, Faculty of Engineering, Software Engineering Dept., Halit Aydın Campus No: 38, Sefaköy–Küçükçekmece, Istanbul, 34295, Turkey
Osman Nuri Uçan
; Istanbul Aydin University, Faculty of Engineering, Electrical & Electronics Engineering Dept., Halit Aydın Campus No: 38, Sefaköy–Küçükçekmece, Istanbul, 34295, Turkey
Sažetak
SOM is a popular artificial neural network algorithm to perform rational clustering on many different data sets. There is a disadvantage of the SOM that can run on a predefined completed data set. Various problems are encountered on a time-stream data sets when clustering by using standard SOM since the time-stream data sets are generated dependent on time. In this study, the Sliding Window feature is included into standard SOM for clustering time-stream data sets. Thus, the combination of SOM and Sliding Window (SOM + SW) gives more accurate results when clustering on time-stream data sets. To prove this, a set of internet usage data from a mobile operator in Turkey is taken to test. The taken data set from the mobile operator is clustered according to the classical SOM then the future data usages of subscribers are estimated. The same data set is applied on the SOM + SW to perform the same simulations.
Ključne riječi
clustering; mobile operators; self-organizing maps (SOM); sliding window; time-stream data sets
Hrčak ID:
190169
URI
Datum izdavanja:
3.12.2017.
Posjeta: 1.803 *