Autonomous data-driven clustering for live data stream

Xiaowei Gu, Plamen Parvanov Angelov

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

8 Citations (SciVal)

Abstract

In this paper, a novel autonomous data-driven clustering approach, called AD-clustering, is presented for live data streams processing. This newly proposed algorithm is a fully unsupervised approach and entirely based on the data samples and their ensemble properties, in the sense that there is no need for user-predefined or problem-specific assumptions and parameters, which is a problem most of the current clustering approaches suffer from. Moreover, the proposed approach automatically evolves its structure according to the experimentally observable streaming data and is able to recursively update its self-defined parameters using only the current data sample; meanwhile, it discards all the previously processed data samples. Experimental results based on benchmark datasets exhibit the higher performance of the proposed fully autonomous approach compared with the comparative approaches requiring user- and problem-specific parameters to be predefined. This new clustering algorithm is a promising tool for further applications in the field of real-time streaming data analytics.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016)
Subtitle of host publicationConference Proceedings
PublisherIEEE Press
Pages1128-1135
Number of pages8
ISBN (Electronic)9781509018970
ISBN (Print)9781509018987
DOIs
Publication statusPublished - 09 Feb 2017
Externally publishedYes

Publication series

Name2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

Keywords

  • Ensemble properties
  • Fully unsupervised clustering
  • Live data streams
  • Recursive update
  • Streaming data analytics

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