Efficiency of k-Means and k-Medoids Clustering Algorithms using Lung Cancer Dataset

The objective of this research work is focused on the right cluster creation of lung cancer data and analyzed the efficiency of k-Means and k-Medoids algorithms. This research work would help the developers to identify the characteristics and flow of algorithms. In this research work is pertinent for the department of oncology in cancer centers. This implementation helps the oncologist to make decision with lesser execution time of the algorithm.It is also enhances the medical care applications. This work is very suitable for selection of cluster development algorithm for lung cancer data analysis.Clustering is an important technique in data mining which is applied in many fields including medical diagnosis to find diseases. It is the process of grouping data, where grouping is recognized by discovering similarities between data based on their features. In this research work, the lung cancer data is used to find the performance of clustering algorithms via its computational time. Considering a limited number attributes of lung cancer data, the algorithmic steps are applied to get results and compare the performance of algorithms. The partition based clustering algorithms k-Means and k-Mediods are selected to analyze the lung cancer data.The efficiency of both the algorithms is analyzed based on the results produced by this approach. The finest outcome of the performance of the algorithm is reported for the chosen data concept.