Projection network, being a non-linear dynamic system itself, has been shown to be superior to static classifiers such as neural networks in some applications where noise is significant. However it is a supervised classifier by nature. To extend its utility for unsupervised classification, this study proposes an unsupervised pattern classifier integrating a clustering algorithm based on DBSCAN and a dynamic classifier based on the projection network. The former is used to form clusters out of un-labeled data and eliminate outliers. Then, significant clusters in terms of size are identified. Subsequently, a system of projection networks is established to recognize all the significant clusters. The unsupervised classifier is tested with three well-known benchmark data sets (by ignoring data labels during training) including the Fisher’s iris data, the heart disease data and the credit screening data and the results are compared to those of supervised classifiers based on the projection network. The difference in performance is small. However, the ability of unsupervised classification comes at a price of a more complex classifier system and the need of data pre-conditioning. The former is because more than one cluster could be formed for a class and therefore more computational units are needed for the classifier, and the latter is because increased similarity of data after clustering increases the chances of numerical instability in the least square algorithm used to initialize the classifier.
An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study
