An application approach of “cluster-classification” in cancer scan images and gene expressions

Medical professionals need a reliable prediction methodology to diagnose cancer and distinguish between the different stages in cancer. Classification is a data mining techniques it mainly classify the dataset based on certain specific criteria. Clustering is another type grouping based on the similarity. These algorithms are applied to cancer dataset to group the patients into either “Carcinoma in situ” (beginning stage) or “Malignant potential” group. Pre-processing techniques have been applied to prepare the raw dataset and identify the relevant attributes for classification. Random test samples have been selected from the pre-processed data to obtain the results. The results are presented and discussed.