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Cancer Diagnosis using Clustering Technique: A Literature Survey

Urvisha Rupapara, Girish Mulchandani

Abstract


DNA microarray analysis is one of the fastest-growing new technologies in the field of genetic research. A microarray dataset contains the expression levels of thousands of genes for an experimental sample. All the genes may not be biologically significant in diagnosing the disease. Clustering algorithm which is an unsupervised machine learning approach has been proposed to select the significant genes. In this paper, we have categories various clustering algorithms found in the literature into distinct categories and also mention hybrid technique to improve the efficiency. The results of K-Means clustering to cluster the genes of the Leukemia dataset for different values of K are analyzed. The value of K (number of cluster) for K-Mean should be predefined.

Keywords


Data Mining, Cluster Analysis, Hybrid Clustering, Cancer Diagnosis

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References


Firdaus, Sabhia, and Md Ashraf Uddin. "A Survey on Clustering Algorithms and Complexity Analysis." International Journal of Computer Science Issues (IJCSI) 12.2 (2015): 62

Jain, Anil K. "Data clustering: 50 years beyond K-means." Pattern recognition letters 31.8 (2010): 651-666.

Bouguettaya, Athman, et al. "Efficient agglomerative hierarchical clustering." Expert Systems with Applications 42.5 (2015): 2785-2797.

Fahad, Adil, et al. "A survey of clustering algorithms for big data: Taxonomy and empirical analysis." Emerging Topics in Computing, IEEE Transactions on 2.3 (2014): 267-279.

Han, Jiawei, and Micheline Kamber. "Data mining: concepts and techniques." (2001).

Abd El-Nasser, Ahmed, Mahboob Shaheen, and Hesham El-Deeb. "Enhanced leukemia cancer classifier algorithm." Science and Information Conference (SAI), 2014. IEEE, 2014.

Palanisamy, Prasath, K. Thangavel, and R. Manavalan. "A novel approach to select significant genes of leukemia cancer data using K-Means clustering." Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on. IEEE, 2013.

Leu, Yungho, Chien-Pan Lee, and Ai-Chen Chang. "A hybrid method for gene selection in microarray datasets." Granular Computing (GrC), 2014 IEEE International Conference on. IEEE, 2014.

Ahmad, Farzana Kabir, Yuhanis Yusof, and Nor Hayati Othman. "Gene selection for high dimensional data using k-means clustering algorithm and statistical approach." Computational Science and Technology (ICCST), 2014 International Conference on. IEEE, 2014.

Popat, Shraddha K., and M. Emmanuel. "Review and Comparative Study of Clustering Techniques." International Journal of Computer Science and Information Technologies 5.1 (2014): 805-812.

Amiria, Saeid, et al. "A general hybrid clustering technique." stat 1050 (2015): 5.

Ackermann, Marcel R., et al. "Analysis of agglomerative clustering." Algorithmica 69.1 (2014): 184-215.

Ahmad, Amir, and Lipika Dey. "A k-mean clustering algorithm for mixed numeric and categorical data." Data & Knowledge Engineering 63.2 (2007): 503-527.

Zheng, Bichen, Sang Won Yoon, and Sarah S. Lam. "Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms." Expert Systems with Applications 41.4 (2014): 1476-1482.

Salama, Gouda I., M. Abdelhalim, and Magdy Abd-elghany Zeid. "Breast cancer diagnosis on three different datasets using multi-classifiers." Breast Cancer (WDBC) 32.569 (2012): 2.


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