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Original scientific paper
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 icon 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

Fulltext: english, pdf (1017 KB) pages 1729-1737 downloads: 281* cite
APA 6th Edition
Çelenk, U., Ertuğrul, D.Ç., Zontul, M. & Uçan, O.N. (2017). Self-organizing maps with sliding window (SOM+SW). Tehnički vjesnik, 24 (6), 1729-1737. https://doi.org/10.17559/TV-20151008153755
MLA 8th Edition
Çelenk, Ulaş, et al. "Self-organizing maps with sliding window (SOM+SW)." Tehnički vjesnik, vol. 24, no. 6, 2017, pp. 1729-1737. https://doi.org/10.17559/TV-20151008153755. Accessed 25 Nov. 2020.
Chicago 17th Edition
Çelenk, Ulaş, Duygu Çelik Ertuğrul, Metin Zontul and Osman Nuri Uçan. "Self-organizing maps with sliding window (SOM+SW)." Tehnički vjesnik 24, no. 6 (2017): 1729-1737. https://doi.org/10.17559/TV-20151008153755
Harvard
Çelenk, U., et al. (2017). 'Self-organizing maps with sliding window (SOM+SW)', Tehnički vjesnik, 24(6), pp. 1729-1737. https://doi.org/10.17559/TV-20151008153755
Vancouver
Çelenk U, Ertuğrul DÇ, Zontul M, Uçan ON. Self-organizing maps with sliding window (SOM+SW). Tehnički vjesnik [Internet]. 2017 [cited 2020 November 25];24(6):1729-1737. https://doi.org/10.17559/TV-20151008153755
IEEE
U. Çelenk, D.Ç. Ertuğrul, M. Zontul and O.N. Uçan, "Self-organizing maps with sliding window (SOM+SW)", Tehnički vjesnik, vol.24, no. 6, pp. 1729-1737, 2017. [Online]. https://doi.org/10.17559/TV-20151008153755
Fulltext: croatian, pdf (1017 KB) pages 1729-1737 downloads: 128* cite
APA 6th Edition
Çelenk, U., Ertuğrul, D.Ç., Zontul, M. & Uçan, O.N. (2017). Samoorganizirane mape s kliznim prozorom (SOM + SW). Tehnički vjesnik, 24 (6), 1729-1737. https://doi.org/10.17559/TV-20151008153755
MLA 8th Edition
Çelenk, Ulaş, et al. "Samoorganizirane mape s kliznim prozorom (SOM + SW)." Tehnički vjesnik, vol. 24, no. 6, 2017, pp. 1729-1737. https://doi.org/10.17559/TV-20151008153755. Accessed 25 Nov. 2020.
Chicago 17th Edition
Çelenk, Ulaş, Duygu Çelik Ertuğrul, Metin Zontul and Osman Nuri Uçan. "Samoorganizirane mape s kliznim prozorom (SOM + SW)." Tehnički vjesnik 24, no. 6 (2017): 1729-1737. https://doi.org/10.17559/TV-20151008153755
Harvard
Çelenk, U., et al. (2017). 'Samoorganizirane mape s kliznim prozorom (SOM + SW)', Tehnički vjesnik, 24(6), pp. 1729-1737. https://doi.org/10.17559/TV-20151008153755
Vancouver
Çelenk U, Ertuğrul DÇ, Zontul M, Uçan ON. Samoorganizirane mape s kliznim prozorom (SOM + SW). Tehnički vjesnik [Internet]. 2017 [cited 2020 November 25];24(6):1729-1737. https://doi.org/10.17559/TV-20151008153755
IEEE
U. Çelenk, D.Ç. Ertuğrul, M. Zontul and O.N. Uçan, "Samoorganizirane mape s kliznim prozorom (SOM + SW)", Tehnički vjesnik, vol.24, no. 6, pp. 1729-1737, 2017. [Online]. https://doi.org/10.17559/TV-20151008153755

Abstracts
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.

Keywords
clustering; mobile operators; self-organizing maps (SOM); sliding window; time-stream data sets

Hrčak ID: 190169

URI
https://hrcak.srce.hr/190169

[croatian]

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