  Lung cancer is the deadliest type of cancer for both men and women. Feature
selection plays a vital role in cancer classification. This paper investigates
the feature selection process in Computed Tomographic (CT) lung cancer images
using soft set theory. We propose a new soft set based unsupervised feature
selection algorithm. Nineteen features are extracted from the segmented lung
images using gray level co-occurence matrix (GLCM) and gray level different
matrix (GLDM). In this paper, an efficient Unsupervised Soft Set based Quick
Reduct (SSUSQR) algorithm is presented. This method is used to select features
from the data set and compared with existing rough set based unsupervised
feature selection methods. Then K-Means and Self Organizing Map (SOM)
clustering algorithms are used to cluster the data. The performance of the
feature selection algorithms is evaluated based on performance of clustering
techniques. The results show that the proposed method effectively removes
redundant features.
