Abstract
In today’s scenario, there is quick evolution in each field which contains majority and distinctive sorts of information. In order to differentiate sample data from the other, the amalgamation of data mining techniques with other useful algorithms is done. Android development is one of the major arena where there is tremendous need to execute these calculations. Combining frequent pattern calculation with clustering is extremely efficacious for android. In this paper the work is done in two levels, initial stage concentrates on generation of clusters and final stage deals with finding the frequent patterns.
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References
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. KDD. Workshop. Text. Min. 400, 109–110 (2000)
Hegland, M.: The Apriori Algorithm CMA. Australian National University, Canberra (2005)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, Burlington (2006)
Singh, R.V., Bhatia, M.S.: (2011) Data clustering with modified k-means Algorithm. In: Proceedings of the IEEE, pp. 717–721, June 2011. ISBN: 978-1-4577-0588-5
Xu, R.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 45–678 (2005)
Matloff N The Art of R Programming. (http://heather.cs.ucdavis.edu/~matloff/132/NSPpart.pdf)
Grolemund G Hands-on programming with R, Garrett (http://droppdf.com/v/KoQlw)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New Jersey (2005)
Velmurugan, T., Santhanam, T.: Computational complexity between k-means and k-medoids clustering algorithms for normal and uniform distributions of data points. J. Comput. Sci. 6(3), 363–368 (2010)
Patel, V.R., Mehta, R.G.: Clustering algorithms: A Comprehensive Survey. In: Proceedings of the International Conference on Electronics Information and Communication Systems Engineering, Jodhpur (2011)
Oyelade, O.J., Oladipupo, O.O., Obagbuwa, I.C.: Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance. Int. J. Comput. Sci. Inf. Secur. 7, 1002 (2010)
Ng, R.T., Han, J.: CLARANS: a method for clustering objects for spatial data mining. IEEE Transact. Knowl. Data Eng. 14(5), 1003–1016 (2002)
Sharmila, M.R.C.: Performance evaluation of clustering algorithms. Int. J. Eng. Trends Technol. 4(7), 2231 (2013)
Revathi, NalinI, s: T performance comparison of various clustering algorithm. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(2), 67–72 (2013)
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Lalwani, A., Banerjee, S., Kindo, M.M., Ali, S.Z. (2018). An Obscure Method for Clustering in Android Using k-Medoid and Apriori Algorithm. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-319-63673-3_9
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DOI: https://doi.org/10.1007/978-3-319-63673-3_9
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