A parallel elastic net clustering algorithm

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Smart Internet of Things, SmartIoT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages40-45
Number of pages6
ISBN (Print)9781538685426
DOIs
Publication statusPublished - 2018 Sep 13
Event2018 IEEE International Conference on Smart Internet of Things, SmartIoT 2018 - Xi'an, China
Duration: 2018 Aug 172018 Aug 19

Publication series

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

Other

Other2018 IEEE International Conference on Smart Internet of Things, SmartIoT 2018
CountryChina
CityXi'an
Period18-08-1718-08-19

All Science Journal Classification (ASJC) codes

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

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