Lung Cancer Data Analysis by k-means and Farthest First Clustering Algorithms

Objective: The objective of this research work is focused on the ethical cluster creation of lung cancer data and analyzed the performance of partition based algorithms. This research work would help the doctors to identify the stages of lung cancer and also enhances the medical care. This work is very convenient to avoid unnecessary biopsy. Methods: Lung Cancer is the form of cancer that has caused the most deaths in both men and women throughout the world. Most of the researchers analyzed the lung cancer dataset using algorithms to find the cluster among the small cell or non-small cell lung cancer in various stages. The very famous two partition based algorithms namely k-Means and FarthestFirst are implemented. A comparative analysis of clustering algorithms is also carried out using two different dataset. The performance of algorithms depends on the time taken to form the estimated clusters. Findings: The performance and cluster formation using the two various kinds of input dataset namely lc.arff, lc.csv are used. The output clusters depends upon the dataset type and algorithms related. The number of initial clusters is chosen by the user. The data points in each cluster are displayed by different colors. The computational complexity is calculated in milliseconds. The k-Means algorithm is efficient for clustering the lung cancer dataset with arff file format. The final outcome of this work is suitable to analyses the behavior of lung cancer in the department of oncology in cancer centers. Our findings are well fit for report preparation and treatment selection of the patients. Application: The created ethical cluster is used for support ingredient of the department of molecular oncology in cancer institution or centers. Ultimate goal of this research work is to find out which type of dataset and algorithm will be most suitable for analysis of lung cancer data.