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An Efficient Hierarchical Clustering Technique for Medical Diagnosis Using KNN Classifier

Pooja Yadav, Anuradha Anuradha, YogitaGigras YogitaGigras

Abstract


In this research article, an intelligent hierarchal clustering technique for medical diagnosis system has been proposed. Various hierarchical clustering techniques and their variants have been very much explored in the field of machine learning. However, these techniques are deterministic, needn't bother with a determined number of clusters and are stable. But, they are not scalable for high dimensional data set due to their non-linear correlations. In this paper, a new approach is proposed for medical data classification based on hierarchical clustering. The proposed technique has the following features (i) In each cycle, rather than ascertaining the centroids for new clusters, new centroids are assessed from centroids in past cycle; and (iii) In every run, rather than combining just a single match of items, various sets are converged in the meantime.

Keywords


Clustering, Hierarchical Agglomerative Clustering, K-Nearest Neighbor (KNN), Feature Selection, Filter and Wrapper Model, Medical Data.

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References


Masoud Makrehchi, “Hierarchical Agglomerative Clustering using common neighbour similarity,” IEEE [2016].

Y. Zhao, G.Karypis , and U. Fayyad, “Hierarchical clustering algorithms for document datasets,” Data Min. Knowl. Discov., vol. 10,pp. 141–168, Mar. 2005.

C. D. Manning, P. Raghavan, and H. Schtze, Introduction to Information Retrieval. New York, NY, USA: Cambridge University Press, 2008.

J. H. Ward, “Hierarchical grouping to optimize an objective function, ”Journal of the American Statistical Association, vol. 58, no. 301,pp. 236–244, 1963.

R. Sibson, “SLINK: an optimally efficient algorithm for the single link cluster method,” Comput. J., vol. 16, no. 1, pp. 30–34, 1973.

D. Krznaric and C. Levcopoulos, “Optimal algorithms for complete linkage clustering in d dimensions,” Theor. Comput. Sci., 2002 Sept, vol. 286, pp. 139–149.

B. S. Everitt, S. Landau, and M. Leese, Cluster Analysis. Wiley, 2009.

V. Tam, A. Santoso, and R. Setiono, “A comparative study of centroid based, neighborhood-based and statistical approaches for effective document categorization,” in Pattern Recognition. Proceedings. 16th International Conference, 2002, vol. 4, pp. 235–238.

D. Liben-Nowell and J. Kleinberg, “The link prediction problem for social networks,” in Proceedings of the twelfth international conference on Information and knowledge management, CIKM ’03, pp. 556–559, ACM, s

R. Dugad and N. Ahuja, “Unsupervised multidimensional hierarchical clustering,”in Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on, vol. 5, pp. 2761–2764 vol.5.

B. Bollobas, Modern Graph Theory. Springer, 2002.

Y. Zhao and G. Karypis, “Evaluation of hierarchical clustering algorithms for document datasets,” in Proceedings of the eleventh international conference on Information and knowledge management, 2002, pp. 515–524, ACM.

D. Eppstein, “Fast hierarchical clustering and other applications of dynamic closest pairs,” J. Experimental Algorithmics, vol. 5, pp. 1–23, June 2000.

A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: A review,” ACM Comput. Surv., vol. 31, pp. 264–323, Sept. 1999.

T. Kurita, “An efficient agglomerative clustering algorithm using a heap,” Pattern Recogn., vol. 24, pp. 205–209, Feb. 1991.550


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