Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2019, Cilt: 3 Sayı: 3, 130 - 136, 28.09.2019

Muhammed Yıldırım Ahmet Çınar

Öz

Kaynakça

  • [1] Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.
  • [2] Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society, 17(4), 49.
  • [3]Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.
  • [4] Nadiammai, G. V., & Hemalatha, M. (2014). Effective approach toward Intrusion Detection System using data mining techniques. Egyptian Informatics Journal, 15(1), 37-50.
  • [5] Roiger, R. J. (2017). Data mining: a tutorial-based primer. CRC Press.
  • [6] Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
  • [7] Jayakameswaraiah, M., Babu, M. M. V., Ramakrishna, S., & Yamuna, M. P. (2016). Computation Accuracy of Hierarchical and Expectation Maximization Clustering Algorithms for the Improvement of Data Mining System.
  • [8] Stevens, R., Casillas, A., 2006. Artificial neural networks. Automated Scoring of Complex Tasks in Computer Based Testing: An Introduction. Lawrence Erlbaum, Mahwah, NJ, 259-312.
  • [9] Cui, X., Zhu, P., Yang, X., Li, K., & Ji, C. (2014). Optimized big data K-means clustering using MapReduce. The Journal of Supercomputing, 70(3), 1249-1259.
  • [10] http://dl.altera.com/?edition=lite

USE OF FPGA FOR REAL-TIME K-MEANS CLUSTERİNG ALGORITHM

Yıl 2019, Cilt: 3 Sayı: 3, 130 - 136, 28.09.2019

Muhammed Yıldırım Ahmet Çınar

Öz

Data mining is important
methods in the data processing step. Due to the fact that computer technologies
are becoming increasingly cheap and their power is increasing day by day, they
allow computers to store data in larger quantities [1]. Owing to the improving
of technology, many transactions are recorded in an electronic device and this
records can be safely stored. This data can easily be accessed when requested. By
means of developing technologies, it is ensured that these processes are
getting more day by day at a lower cost. Therefore, it is of great importance
to be able to process these data of high size. Clustering algorithms that
aggregate the data in the database under groups or clusters to bring together
objects with similar properties have a great deal of data mining proposition. In
this paper, it is aimed to collect 2 clusters based on the similarities of 60
data obtained from 2 different wheat varieties using k-means clustering
algorithm based on Fpga architecture. Since the FPGA architecture has the
ability to perform parallel processing, it will shorten the processing time and
so efficiency will increase. Also, the ability to use FPGA’s over and over
again provides an extra advantage. The proposed system is designed using the
verilog hardware identification language on the DE2_115 Fpga board.

Anahtar Kelimeler

Data Mining Fpga K-means Verilog

Kaynakça

  • [1] Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.
  • [2] Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society, 17(4), 49.
  • [3]Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.
  • [4] Nadiammai, G. V., & Hemalatha, M. (2014). Effective approach toward Intrusion Detection System using data mining techniques. Egyptian Informatics Journal, 15(1), 37-50.
  • [5] Roiger, R. J. (2017). Data mining: a tutorial-based primer. CRC Press.
  • [6] Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
  • [7] Jayakameswaraiah, M., Babu, M. M. V., Ramakrishna, S., & Yamuna, M. P. (2016). Computation Accuracy of Hierarchical and Expectation Maximization Clustering Algorithms for the Improvement of Data Mining System.
  • [8] Stevens, R., Casillas, A., 2006. Artificial neural networks. Automated Scoring of Complex Tasks in Computer Based Testing: An Introduction. Lawrence Erlbaum, Mahwah, NJ, 259-312.
  • [9] Cui, X., Zhu, P., Yang, X., Li, K., & Ji, C. (2014). Optimized big data K-means clustering using MapReduce. The Journal of Supercomputing, 70(3), 1249-1259.
  • [10] http://dl.altera.com/?edition=lite
Toplam 10 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Articles
Yazarlar

Muhammed Yıldırım 0000-0003-1866-4721

Ahmet Çınar

Yayımlanma Tarihi 28 Eylül 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 3 Sayı: 3

Kaynak Göster

IEEE M. Yıldırım ve A. Çınar, “USE OF FPGA FOR REAL-TIME K-MEANS CLUSTERİNG ALGORITHM”, IJESA, c. 3, sy. 3, ss. 130–136, 2019.
 Kapak Resmi İndir

ISSN 2548-1185
e-ISSN 2587-2176
Period: Quarterly
Founded: 2016
Publisher: Nisantasi University
e-mail:ilhcol@gmail.com