A parallel elastic net clustering algorithm

Tzu Yi Feng, Chun Wei Tsai, Ming Chao Chiang, Chu Sing Yang

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

The elastic net clustering algorithm (ENCA) can typically provide an effective way for classifying non-linearly separable data. However, the computation time it takes will be significantly increased for large datasets. To deal with this issue, a parallel version of the ENCA, built on the Apache Spark framework, called parallel elastic net clustering algorithm (PENCA), is presented in this paper. To evaluate the performance of the proposed algorithm, it is compared with ENCA and two well-known clustering algorithms, k-means and genetic k-means algorithm (GKA). The results show that PENCA not only outperforms k-means and GKA in terms of the accuracy rate, it also provides an efficient way to reduce the response time of ENCA-based clustering algorithms for large-scale datasets.

原文English
主出版物標題Proceedings - 2018 IEEE International Conference on Smart Internet of Things, SmartIoT 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面40-45
頁數6
ISBN(列印)9781538685426
DOIs
出版狀態Published - 2018 九月 13
事件2018 IEEE International Conference on Smart Internet of Things, SmartIoT 2018 - Xi'an, China
持續時間: 2018 八月 172018 八月 19

出版系列

名字Proceedings - 2018 IEEE International Conference on Smart Internet of Things, SmartIoT 2018

Other

Other2018 IEEE International Conference on Smart Internet of Things, SmartIoT 2018
國家China
城市Xi'an
期間18-08-1718-08-19

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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