**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**31824

##### Improved Wavelet Neural Networks for Early Cancer Diagnosis Using Clustering Algorithms

**Authors:**
Zarita Zainuddin,
Ong Pauline

**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.

**Keywords:**
Clustering,
microarray,
symmetry,
wavelet neural networks.

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1072255

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