Clustering of lung cancer data using Foggy K-means

In the medical field, huge data is available, which leads to the need of a powerful data analysis tool for extraction of useful information. Several studies have been carried out in data mining field to improve the capability of data analysis on huge datasets. Cancer is one of the most fatal diseases in the world. Lung Cancer with high rate of accurance is one of the serious problems and biggest killing disease in India. Prediction of occurance of the lung cancer is very difficult because it depends upon multiple attributes which could not be analyzedeasily. In this paper a real time lung cancer dataset is taken from SGPGI (Sanjay Gandhi Post Graduate Institute of Medical Sciences) Lucknow. A realtime dataset is always associated with its obvious challenges such as missing values, highly dimensional, noise, and outlier, which is not suitable for efficient classification. A clustering approach is an alternative solution to analyze the data in an unsupervised manner. In this current research work main focus is to develop a novel approach to create accurate clusters of desired real time datasets called Foggy K-means clustering. The result of the experiment indicates that foggy k-means clustering algorithm gives better result on real datasets as compared to simple k-means clustering algorithm and provides a better solution to the real world problem.