Cancer is known as one of the dangerous diseases in this world in the current time frame. The medical world is trying to be as optimal as possible in order to cure a cancer patient. Cancer have different kind of stages and hence have different levels of treatment as well. Drugs for such kind of diseases have a complex architecture system and they are not very easy to understand for a common man. This research paper aims to create clusters for drugs of cancer disease so that they can be even identified by common man. The architecture of drugs are often referred as Genomics and it has several key features like IC 50 value. The presented architecture has enhanced traditional K-Means algorithm by adding inner groups in the outer cluster. The evaluation has been made on the basis of Cluster Accuracy and error rate. Any clustering technique works with the threshold segmentation and value analysis and without any approval from external component, they can’t be termed as accurate and precise. The proposed architecture understands this requirement and hence it has utilized support vector machine for the cluster classification in order to understand the preciseness of the clustering.
Author(s): Swathi Muppalaneni