DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
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. A clustering approach is an alternative solution to analyze the data in an unsupervised research. In this work, the main focus is to develop a novel approach to create accurate clusters of desired real time datasets using k-Means and k-Medoids clustering algorithms. The result of the experiment indicates that k-Means clustering algorithm gives better result on real datasets as compared with simple k-Medoids algorithm and provides a better solution in the Medical domain.
