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Autonomous Data Density based clustering method

Angelov, Plamen P., Gu, Xiaowei, Gutierrez, German, Iglesias, Jose Antonio, Sanchis, Araceli (2016) Autonomous Data Density based clustering method. In: 2016 International Joint Conference on Neural Networks (IJCNN). . pp. 2405-2413. IEEE ISBN 978-1-5090-0621-2. E-ISBN 978-1-5090-0620-5. (doi:10.1109/IJCNN.2016.7727498) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90178)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1109/IJCNN.2016.7727498

Abstract

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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/IJCNN.2016.7727498
Uncontrolled keywords: Clustering methods; Clustering algorithms; Kernel; Data analysis; Data mining; Measurement; Electronic mail; fully autonomous clustering; data density; mutual distribution; data analytics
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 13 Sep 2021 11:09 UTC
Last Modified: 14 Sep 2021 09:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90178 (The current URI for this page, for reference purposes)
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