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Clinical data has been employed as the major factor for traditional cancer prognosis. However, this classic approach may be ineffective for analyzing morphologically indistinguishable tumor subtypes. As such, the microarray technology emerges as the promising alternative. Despite a large number of microarray studies, the actual clinical application of gene expression data analysis remains limited...
In this paper we present an original framework to extract representative groups from a dataset, and we validate it over a novel case study. The framework specifies the application of different clustering algorithms, then several statistical and visualisation techniques are used to characterise the results, and core classes are defined by consensus clustering. Classes may be verified using supervised...
In this paper, we propose a novel objective function called the adaptive Fuzzy Weighted Sum Validity Function(FWSVF), which is a merged weight of the several fuzzy cluster validity functions, including XB, PE, PC and PBMF. The improved validity function is more efficient than others. Furthermore, we present a Mixed Strategy Evolutionary Clustering Algorithm based adaptive validity function(AMSECA),...
Present study has brought out a comparison of PCA and fuzzy clustering techniques in classifying protein profiles (chromatogram) of homogenates of different tissue origins: Ovarian, Cervix, Oral cancers, which were acquired using HPLC-LIF (High Performance Liquid Chromatography-Laser Induced Fluorescence) method developed in our laboratory. Study includes 11 chromatogram spectra each from oral, cervical,...
Class discovery from gene expression data is an important task for cancer diagnosis. In this paper, we present a new framework for class discovery. The new framework integrates the perturbation technique, the cluster ensemble approach, and the cluster validity index. Specifically, it first generates a set of perturbed datasets from the original microarray data. Then, the Neural Gas, which serves as...
Normalization before clustering is often needed for proximity indices, such as Euclidian distance, which are sensitive to differences in the magnitude or scales of the attributes. The goal is to equalize the size or magnitude and the variability of these features. This can also be seen as a way to adjust the relative weighting of the attributes. In this context, we present a first large scale data...
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