@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},
	}