Main Article Content

Abstract

The research for suggesting an ice cream for a diabetic patient is carried out in data mining by using clustering and mapping between the data for ice cream and diabetic patients. Here, mapping of ice cream dataset with diabetic patient dataset is done by using MFCA, which is proposed and explained in this paper. The results obtained from MCFA algorithm and the new proposed algorithm are explained and verified and it is observed that they are having the relevance.

Keywords

Modified Cluster Formation Algorithm Newly proposed Algorithm Ice cream Diabetic Patient.

Article Details

How to Cite
Gaikwad, S. M., Mulay, P., & Joshi, R. R. (2015). 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. International Journal of Students’ Research in Technology & Management, 3(6), 410–412. https://doi.org/10.18510/ijsrtm.2015.366

References

  1. S. Chauhan, M. Imdad, W. Sintunavarat, and Y. Shen, "Unified fixed point theorems for mappings in fuzzy metric spaces via implicit relations," Journal of the Egyptian Mathematical Society, 2014. DOI: https://doi.org/10.1016/j.joems.2014.05.008
  2. T. Y. Chen and J. H. Huang, "Application of data mining in a global optimization algorithm," Advances in Engineering Software, vol. 66, pp. 24-33, 2013. DOI: https://doi.org/10.1016/j.advengsoft.2012.11.019
  3. M. J. Cracknell and A. M. Reading, "Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information," Computers & Geosciences, vol. 63, pp. 22-33, 2014. DOI: https://doi.org/10.1016/j.cageo.2013.10.008
  4. V. M. da Silva, V. P. R. Minim, M. A. M. Ferreira, P. H. d. P. Souza, L. E. d. S. Moraes, and L. A. Minim, "Study of the perception of consumers in relation to different ice cream concepts," Food Quality and Preference, vol. 36, pp. 161-168, 2014. DOI: https://doi.org/10.1016/j.foodqual.2014.04.008
  5. J. P. Donate, P. Cortez, G. G. Sánchez, and A. S. de Miguel, "Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble," Neurocomputing, vol. 109, pp. 27-32, 2013. DOI: https://doi.org/10.1016/j.neucom.2012.02.053
  6. D. N. Flynn, "Building a Better Model: A Novel Approach for Mapping Organisational and Functional Structure," Procedia Computer Science, vol. 44, pp. 194-203, 2015. DOI: https://doi.org/10.1016/j.procs.2015.03.003
  7. Ghosh and S. Mitra, "Clustering large data with uncertainty," Applied Soft Computing, vol. 13, pp. 1639-1645, 2013. DOI: https://doi.org/10.1016/j.asoc.2012.12.036
  8. M. Grossi, R. Lazzarini, M. Lanzoni, and B. Riccò, "A novel technique to control ice cream freezing by electrical characteristics analysis," Journal of Food Engineering, vol. 106, pp. 347-354, 2011. DOI: https://doi.org/10.1016/j.jfoodeng.2011.05.035
  9. K. Jung, K. C. Morris, K. W. Lyons, S. Leong, and H. Cho, "Mapping Strategic Goals and Operational Performance Metrics for Smart Manufacturing Systems," Procedia Computer Science, vol. 44, pp. 184-193, 2015. DOI: https://doi.org/10.1016/j.procs.2015.03.051
  10. T. Kanit, S. Forest, D. Jeulin, F. N’Guyen, and S. Singleton, "Virtual improvement of ice cream properties by computational homogenization of microstructures," Mechanics Research Communications, vol. 38, pp. 136-140, 2011. DOI: https://doi.org/10.1016/j.mechrescom.2011.01.005
  11. C. Lemke and B. Gabrys, "Meta-learning for time series forecasting and forecast combination," Neurocomputing, vol. 73, pp. 2006-2016, 2010. DOI: https://doi.org/10.1016/j.neucom.2009.09.020
  12. M. K. Pakhira, "Finding Number of Clusters before Finding Clusters," Procedia Technology, vol. 4, pp. 27-37, 2012. DOI: https://doi.org/10.1016/j.protcy.2012.05.004
  13. I. Railean, P. Lenca, S. Moga, and M. Borda, "Closeness Preference – A new interestingness measure for sequential rules mining," Knowledge-Based Systems, vol. 44, pp. 48-56, 2013. DOI: https://doi.org/10.1016/j.knosys.2013.01.025
  14. Preeti Mulay, Dr. Parag A. Kulkarni, “Knowledge augmentation via incremental clustering, new technology for effective knowledge managementâ€, ACM digital library, International journal of business information systems, vol. 12, issue 1, Dec 2013. DOI: https://doi.org/10.1504/IJBIS.2013.050660
  15. Suhas Machhindra Gaikwad, Dr. Preeti Mulay, Rahul Raghvendra Joshi. "Analytical Hierarchy Process to Recommend an Ice Cream to a Diabetic Patient based on Sugar Content in it." procedia Elsevier (2015). http://www.statcrunch.com.
  16. Prachi M. Joshi, Dr. Parag A. Kulkarni A Novel Approach for Clustering based on Pattern Analysis International Journal of Computer Applications (0975 – 8887) Volume 25– No.4, July 2011 DOI: https://doi.org/10.5120/3023-4089