A novel approach for extracting ideal exemplars by clustering for massive time-ordered datasets
Yükleniyor...
Tarih
2017-07-30
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
TÜBİTAK
Erişim Hakkı
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-ShareAlike 3.0 United States
Attribution-NonCommercial-ShareAlike 3.0 United States
Özet
The number and length of massive datasets have increased day by day and this yields more complex machine
learning stages due to the high computational costs. To decrease the computational cost many methods were proposed
in the literature such as data condensing, feature selection, and filtering. Although clustering methods are generally
employed to divide samples into groups, another way of data condensing is by determining ideal exemplars (or prototypes),
which can be used instead of the whole dataset. In this study, first the efficiency of traditional data condensing by
clustering approach was confirmed according to obtained accuracies and condensing ratios in 9 different synthetic or real
batch datasets. This approach was then improved to be employed in time-ordered datasets. In order to validate the
proposed approach, 23 different real time-ordered datasets were used in experiments. Achieved mean RMSEs were 0.27
and 0.29 by employing the condensed (mean condensed ratio was 97.17%) and the whole datasets, respectively. Obtained
results showed that higher accuracy rates and condensing ratios were achieved by the proposed approach.
Açıklama
Anahtar Kelimeler
Data Condensing, Prototype Extracting, Clustering, Massive Datasets, Time-Ordered Datasets
Kaynak
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
25
Sayı
4
Künye
Ertuğrul, Ö. F. (2017). A novel approach for extracting ideal exemplars by clustering for massive
time-ordered datasets. Turkish Journal of Electrical Engineering & Computer Sciences, 25 (4), pp. 2614 – 2634. https://doi.org/10.3906/elk-1602-341
