A half-split grid clustering algorithm by simulating cell division

Wenxiang Dou, Jinglu Hu

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

3 Citations (Scopus)

Abstract

Clustering, one of the important data mining techniques, has two main processing methods on data-based similarity clustering and space-based density grid clustering. The latter has more advantage than the former on larger and multiple shape and density dataset. However, due to a global partition of existing grid-based methods, they will perform worse when there is a big difference on the density of clusters. In this paper, we propose a novel algorithm that can produces appropriate grid space in different density regions by simulating cell division process. The time complexity of the algorithm is O(n) in which n is number of points in dataset. The proposed algorithm will be applied on popular chameleon datasets and our synthetic datasets with big density difference. The results show our algorithm is effective on any multi-density situation and has scalability on space optimization problems.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2183-2189
Number of pages7
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - 2014 Sep 3
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 2014 Jul 62014 Jul 11

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
CountryChina
CityBeijing
Period14/7/614/7/11

Keywords

  • data clustering
  • grid clustering
  • unsupervised learningt

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Dou, W., & Hu, J. (2014). A half-split grid clustering algorithm by simulating cell division. In Proceedings of the International Joint Conference on Neural Networks (pp. 2183-2189). [6889720] (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889720