Samoorganizirane mape s kliznim prozorom (SOM + SW)
| العنوان: | Samoorganizirane mape s kliznim prozorom (SOM + SW) |
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| المؤلفون: | Duygu Çelik Ertuğrul, Metin Zontul, Ulaş Çelenk, Osman N. Ucan |
| المصدر: | Tehnički Vjesnik, Vol 24, Iss 6, Pp 1729-1737 (2017) Tehnički vjesnik Volume 24 Issue 6 |
| بيانات النشر: | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | 021103 operations research, Computer science, business.industry, 0211 other engineering and technologies, General Engineering, Pattern recognition, 02 engineering and technology, clustering, mobile operators, self-organizing maps (SOM), sliding window, time-stream data sets, grupiranje, klizni prozor, mobilni operateri, samoorganizirane mape (SOM), vremenski tok skupova podataka, ComputingMethodologies_PATTERNRECOGNITION, lcsh:TA1-2040, Sliding window protocol, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Cluster analysis, business, lcsh:Engineering (General). Civil engineering (General) |
| الوصف: | SOM je popularan algoritam umjetne neuronske mreže za obavljanje racionalnog grupiranja na mnogim različitim skupovima podataka. Postoji nedostatak SOM-e koja se može izvoditi na unaprijed definiranom dovršenom skupu podataka. Na vremenskim tokovima skupova podataka pojavljuju se razni problemi prilikom grupiranja pomoću standardne SOM-e jer se vremenski tokovi podataka generiraju ovisno o vremenu. U ovoj studiji značajka kliznog prozora uključena je u standardnu SOM-u za grupiranje vremenskih tokova podataka. Stoga, kombinacija SOM i kliznog prozora (SOM + SW) daje točnije rezultate prilikom grupiranja podataka na vremenskom toku skupova podataka. Da bi se to dokazalo, testiran je skup podataka o uporabi interneta mobilnog operatora u Turskoj. Uzeti skup podataka mobilnog operatera grupiran je prema klasičnoj SOM-i, a zatim je procijenjena buduća uporaba podataka pretplatnika. Isti skup podataka primijenjen je na SOM + SW za izvođenje istih simulacija. 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1848-6339 1330-3651 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::772af90a7f042d7ec6ff32408c6784a1 https://hrcak.srce.hr/file/280267 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....772af90a7f042d7ec6ff32408c6784a1 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 18486339 13303651 |
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