Abstract
Diabetes Mellitus is caused due to disorders of metabolism and its one of the most common diseases in the world today, and growing. Threshold Based Clustering Algorithm (TBCA) is applied to medical data received from practitioners and presented in this paper. Medical data consist of various attributes. TBCA is formulated to effectually compute impactful attributes related to Mellitus, for further decisions. TBCAs primary focus is on computation of Threshold values, to enhance accuracy of clustering results.
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References
K.R. Lakshmi, S.P. Kumar, Utilization of data mining techniques for prediction of diabetes disease survivability. Int. J. Sci. Eng. Res. 4(6), 933–940 (2013)
D.S. Vijayarani, M.P. Vijayarani, Detecting outliers in data streams using clustering algorithms. Int. J. Innov. Res. Comput. Commun. Eng. 1(8), 1749–1759 (2013)
P. Mulay, P.A. Kulkarni, Knowledge augmentation via incremental clustering: new technology for effective knowledge management. Int. J. Bus. Inf. Syst. 12(1), 68–87 (2013)
P.A. Kulkarni, P. Mulay, Evolve systems using incremental clustering approach. Evol. Syst. 4(2), 71–85 (2013)
M. Borhade, P. Mulay, Online interactive data mining tool. Proc. Comput. Sci. 50, 335–340 (2015)
P. Mulay, Threshold computation to discover cluster structure: a new approach. Int. J. Electr. Comput. Eng. (IJECE), 6(1) (2016)
R.J. Singh, W. Singh, Data mining in healthcare for diabetes mellitus. Int. J. Sci. Res. (IJSR) 3(7), 1993–1998 (2014)
S.M. Gaikwad, P. Mulay, R.R. Joshi, Attribute visualization and cluster mapping with the help of new proposed algorithm and modified cluster formation algorithm to recommend an ice cream to the diabetic patient based on sugar contain in it. Int. J. Appl. Eng. Res. 10 (2015)
M.W. Berry, J.J. Lee, G. Montana, S. Van Aelst, R.H. Zamar, Special issue on advances in data mining and robust statistics. Comput. Stat. Data Anal. 93(C), 388–389 (2016)
M.S. Tejashri, N. Giri, Prof S.R. Todamal, Data mining approach for diagnosing type 2 diabetes. Int. J. Sci. Eng. Technol. 2(8), 191–194 (2014)
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Mulay, P., Joshi, R.R., Anguria, A.K., Gonsalves, A., Deepankar, D., Ghosh, D. (2017). Threshold Based Clustering Algorithm Analyzes Diabetic Mellitus. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-10-3156-4_3
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DOI: https://doi.org/10.1007/978-981-10-3156-4_3
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