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Unsupervised Data Classification for Convex and Non Convex Classes
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 24 - Number 1 |
| Year of Publication: 2011 |
| Authors: Fairouz Lekhal, Mohamed El Hitmy, Ouafae El Melhaoui |
10.5120/2917-3843
|
Fairouz Lekhal, Mohamed El Hitmy, Ouafae El Melhaoui . Unsupervised Data Classification for Convex and Non Convex Classes. International Journal of Computer Applications. 24, 1 ( June 2011), 8-15. DOI=10.5120/2917-3843
Abstract
We present in this work, a new unsupervised data classification technique based on a three steps system: Split, Clean and Merge. In this system, the classes are represented by a set of subclasses that we call prototypes. The prototypes are created in an incremental way from the initial data set. No prior knowledge on the classes is required. The data are presented to the system one by one in an arbitrary way. The system built on a neural network strategy ends up by acquiring knowledge on the data and gathers the data into a set of real classes which may have a non convex structure. The method proposed is compared to the fuzzy C-means ‘FCM’ and fuzzy min max clustering ‘FMMC’ methods through a number of simulations. The results obtained by the proposed method are very good.
References
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