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Abstract
Cancer research is the one of the major research areas in the medical field. Pointed out the exact tumour types provides an optimized solution for the better treatment and toxicity minimization due to medicines on the patients. To get a clear picture on the insight of a problem, a clear cancer classification analysis system needs to be pictured followed by a systematic approach to analyse Global Gene Expression which provides an optimized solution for the identified problem area. Molecular diagnostics provides a promising option of systematic human cancer classification, but these tests are not widely applied because characteristic molecular markers for most solid tumours have yet to be identified. Recently, DNA microarray-based tumour gene expression profiles have been used for cancer diagnosis. Â Existing system focussed in ranging from old nearest neighbour analysis to support vector machine manipulation for the learning portion of the classification model. Supervised Multi Attribute Clustering Algorithm, which can manage knowledge, attributes coming two different knowledge streams. Our proposed system takes the input from multiple sources, creates an ontological store, cluster the data with attribute match association rule and followed by classification with the knowledge acquired.
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