Unsupervised classification of data streams based on typicality and eccentricity data analytics

Costa, Bruno Sielly Jales and Bezerra, Clauber Gomes and Guedes, Luiz Affonso and Angelov, Plamen Parvanov (2016) Unsupervised classification of data streams based on typicality and eccentricity data analytics. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ) :. IEEE, Vancouver Canada, pp. 58-63. ISBN 9781509006250

[thumbnail of bare_conf]
PDF (bare_conf)
bare_conf.pdf - Published Version
Available under License Creative Commons Attribution-NonCommercial.

Download (932kB)


In this paper, we propose a novel approach to unsupervised and online data classification. The algorithm is based on the statistical analysis of selected features and development of a self-evolving fuzzy-rule-basis. It starts learning from an empty rule basis and, instead of offline training, it learns “on-the-fly”. It is free of parameters and, thus, fuzzy rules, number, size or radius of the classes do not need to be pre-defined. It is very suitable for the classification of online data streams with realtime constraints. The past data do not need to be stored in memory, since that the algorithm is recursive, which makes it memory and computational power efficient. It is able to handle concept-drift and concept-evolution due to its evolving nature, which means that, not only rules/classes can be updated, but new classes can be created as new concepts emerge from the data. It can perform fuzzy classification/soft-labeling, which is preferred over traditional crisp classification in many areas of application. The algorithm was validated with an industrial pilot plant, where online calculated period and amplitude of control signal were used as input to a fault diagnosis application. The approach, however, is generic and can be applied to different problems and with much higher dimensional inputs. The results obtained from the real data are very significant.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ID Code:
Deposited By:
Deposited On:
27 Sep 2016 12:58
Last Modified:
10 Jan 2024 00:42