Open Access Open Access  Restricted Access Subscription or Fee Access

A Novel k-Singular Value Decomposition Clustering Approach for Cancer Diagnosis

Dr. R. Anusuya, Dr. M. Mahalakshmi


In recent advancement the diagnosing and analyzing is important process in medical field. This research concentrates on detecting cancer by Clustering analysis, it is a process of assigning set of objects into groups which is termed as clusters. Cluster analysis is used to differentiate the types of cluster classification in a given image. The considered input image may be in any shape and size, but the cluster size shape and intensity is based on the time based feature, for each image that was taken over time. The main objective of the research is to diagnosis the cancer patients. Nowadays cancer becomes major disease among many peoples all over the world. For early diagnosis of the cancer patients, clustering data mining based K- Singular Value Decomposition (SVD) algorithm is considered. For experimental purpose the cancer dataset carried out from the web data repository. To select the appropriate clustering, the data mining technique is a challenge for diagnosis of cancer. This research becomes very much helpful in diagnosis cancer and also for early treatment.


Cluster Analysis, k-Means Algorithm, Singular Value Decomposition, Cancer Diagnosing.

Full Text:



Dharmarajan, A., & Velmurugan, T. (2015). Lung cancer data analysis by k-means and farthest first clustering algorithms. Indian Journal of Science and Technology, 8(15).

Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. Advances in neural information processing systems, 2, 849-856.

Sharan, R., & Shamir, R. (2000, August). CLICK: a clustering algorithm with applications to gene expression analysis. In Proc Int Conf Intell Syst Mol Biol (Vol. 8, No. 307, p. 16).

Jiang, D., Tang, C., & Zhang, A. (2004). Cluster analysis for gene expression data: a survey. IEEE Transactions on knowledge and data engineering, 16(11), 1370-1386.

Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., ... & Golub, T. R. (1999). Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences, 96(6), 2907-2912.

Golub, G. H., & Van Loan, C. F. (1996). The singular value decomposition and unitary matrices. Matrix Computations, 70-71.

Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences, 95(25), 14863-14868.

Tomfohr, J., Lu, J., & Kepler, T. B. (2005). Pathway level analysis of gene expression using singular value decomposition. BMC bioinformatics, 6(1), 1.

Qi, R., & Zhou, S. (2014). A comparative study of algorithms for grouping cancer data. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 1).

Chen, D., Xing, K., Henson, D., Sheng, L., Schwartz, A. M., & Cheng, X. (2009). Developing prognostic systems of cancer patients by ensemble clustering. BioMed Research International, 2009.

Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis (Vol. 344). John Wiley & Sons.

Krishnaiah, V., Narsimha, D. G., & Chandra, D. N. S. (2013). Diagnosis of lung cancer prediction system using data mining classification techniques. International Journal of Computer Science and Information Technologies, 4(1), 39-45.

Kharya, S. (2012). Using data mining techniques for diagnosis and prognosis of cancer disease. arXiv preprint arXiv:1205.1923.

Rani, K. U. (2010). Parallel approach for diagnosis of breast cancer using neural network technique. International Journal of Computer Applications, 10(3), 1-5.

Zubi, Z. S., & Saad, R. A. (2014). Improves Treatment Programs of Lung Cancer Using Data Mining Techniques. Journal of Software Engineering and Applications, 7(2), 69.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.