Clustering Lung Cancer Data by k-Means and k- Medoids Algorithms

In medical field, huge data is available, which leads to the need of a powerful data analysis for the extraction of useful information. Several studies have been carried out in the domain of data mining to improve the capability of data analysis on large datasets. Cancer is one of the most fatal diseases in the world. Lung Cancer with high rate of occurrence is one of the serious problems and biggest mortality disease in India. Prediction of occurrence of the lung cancer is very difficult because it depends upon multiple attributes which could not be analyzed easily. In this research work a real time lung cancer dataset is collected from private medical laboratory in and around Tamil Nadu. A real time dataset is always associated with its obvious challenges such as missing values, highly dimensional, noise, and outlier, which are not suitable for efficient classification.