@article{(Open Science Index):https://publications.waset.org/pdf/9166, title = {Improved Wavelet Neural Networks for Early Cancer Diagnosis Using Clustering Algorithms}, author = {Zarita Zainuddin and Ong Pauline}, country = {}, institution = {}, abstract = {Wavelet neural networks (WNNs) have emerged as a vital alternative to the vastly studied multilayer perceptrons (MLPs) since its first implementation. In this paper, we applied various clustering algorithms, namely, K-means (KM), Fuzzy C-means (FCM), symmetry-based K-means (SBKM), symmetry-based Fuzzy C-means (SBFCM) and modified point symmetry-based K-means (MPKM) clustering algorithms in choosing the translation parameter of a WNN. These modified WNNs are further applied to the heterogeneous cancer classification using benchmark microarray data and were compared against the conventional WNN with random initialization method. Experimental results showed that a WNN classifier with the MPKM algorithm is more precise than the conventional WNN as well as the WNNs with other clustering algorithms. }, journal = {International Journal of Computer and Information Engineering}, volume = {3}, number = {11}, year = {2009}, pages = {2550 - 2556}, ee = {https://publications.waset.org/pdf/9166}, url = {https://publications.waset.org/vol/35}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 35, 2009}, }