Autonomous data density based clustering method

Plamen Parvanov Angelov, Xiaowei Gu, German Gutierrez, Jose Antonio Iglesias, Araceli Sanchis

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

9 Citations (SciVal)


It is well known that clustering is an unsupervised machine learning technique. However, most of the clustering methods need setting several parameters such as number of clusters, shape of clusters, or other user- or problem-specific parameters and thresholds. In this paper, we propose a new clustering approach which is fully autonomous, in the sense that it does not require parameters to be pre-defined. This approach is based on data density automatically derived from their mutual distribution in the data space. It is called ADD clustering (Autonomous Data Density based clustering). It is entirely based on the experimentally observable data and is free from
restrictive prior assumptions.
This new method exhibits highly accurate clustering performance. Its performance is compared on benchmarked data sets with other competitive alternative approaches. Experimental results demonstrate that ADD clustering significantly outperforms other clustering methods yet does not require restrictive user- or problem-specific parameters or assumptions. The new clustering method is a solid basis for further applications in the field of data analytics.
Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherIEEE Press
Number of pages9
ISBN (Electronic)9781509006199
Publication statusPublished - 03 Nov 2016
Externally publishedYes

Publication series

NameProceedings of the International Joint Conference on Neural Networks


  • Data analytics
  • Data density
  • Fully autonomous clustering
  • Mutual distribution


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