Autonomous Data Density based Clustering Method. Angelov, P., Gu, X., Gutierrez, G., Iglesias, J. A., & Sanchis, A. In 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, BC, Canada, July 24-29, 2016, pages 2405-2413, 2016. doi abstract bibtex 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.
@inproceedings{AngelovWCCI2016,
author = {Plamen Angelov and Xiaowei Gu and German Gutierrez and Jose Antonio Iglesias and Araceli Sanchis},
title = {Autonomous Data Density based Clustering Method},
booktitle = {2016 International Joint Conference on Neural Networks, {IJCNN} 2016, Vancouver, BC, Canada, July 24-29, 2016},
pages = {2405-2413},
year = {2016},
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.},
doi = {10.1109/IJCNN.2016.7727498}
}
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