Clustering Analysis of Premier Research Fields

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    The clusterization is one of methods which utilized to grouping a dataset which has a specific characteristics value. The processed data can be numerical or non-numerical data. Non-numeric data must be transformed first into numerical data. The case study in this study was to group research from six fields of science. The research data is non-numerical data is converted into the research contributions percentage in the science field. Utilized the c-means algorithm, the data was successfully grouped into three excellent research fields. The aim of the clustering is to know how many researchers in one cluster. Dataset is processed by utilizing the c-means algorithm to generated 3 clusters, they are an expeditious technology, entrepreneur and economic creative development, social engineering and strategic area infrastructure development. The data clustering result is presented in the graphic form by utilized the studio Rapidminer application.

     


     

  • Keywords


    Clustering, Premier research fields, C-Means algorithm, Euclidean distance

  • References


      [1] K. Vadim, Overview of different approaches to solving problems of data mining, in: Procedia Comput. Sci., Elsevier B.V., 2018: pp. 234–239. http://doi:10.1016/j.procs.2018.01.036.

      [2] K.A.A. Nazeer, M.P. Sebastian, Improving the Accuracy and Efficiency of the k-means Clustering Algorithm, in: Proc. World Congr. Eng., 2009: pp. 1–5.

      [3] E.M. Jane, E.G. Dharma, P. Raj, "SBKMMA : Sorting Based K Means and Median Based Clustering Algorithm Using Multi Machine Technique for Big Data", Int. J. Comput. Vol.28, (2018) pp:1–7.

      [4] R. Wang, W. Ji, M. Liu, X. Wang, J. Weng, S. Deng, "Review on mining data from multiple data sources", Pattern Recognit. Lett. (2018) pp:1–9. http://doi:10.1016/j.patrec.2018.01.013.

      [5] A.C. Fabregas, B.D. Gerardo, "Enhanced Initial Centroids for K-means Algorithm", I.J. Inf. Technol. Comput. Sci. Vol.1, (2017) pp:26–33. http://doi:10.5815/ijitcs.2017.01.04.

      [6] N. Aggarwal, K. Aggarwal, Kirti, gupta, "Comparative Analysis of k-means and Enhanced K-means clustering algorithm for data mining", Int. J. Sci. Eng. Res. Vol.3, (2012). https://pdfs.semanticscholar.org/c752/009f6372e89aa1f1417857b671b242a58854.pdf.

      [7] H. Guruler, "A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with K-Mean", Neural Comput. Appl. (2016). http://doi:10.1007/s00521-015-2142-2.

      [8] S. a Yang, J. Yoon, K. Kim, Y. Park, "Measurements of Morphological and Biophysical Alterations in Individual Neuron Cells Associated with Early Neurotoxic Effects in Parkinson ’ s Disease", Int. Soc. Adv. Cytom. (2017) pp:510–518. http://doi:10.1002/cyto.a.23110.

      [9] H. Jung, K. Chung, "Knowledge-based dietary nutrition recommendation for obese management", Springer Sci. (2016) pp:29–42. http://doi:10.1007/s10799-015-0218-4.

      [10] A. Alsayat, H. El-Sayed, Efficient genetic K-Means clustering for health care knowledge discovery, in: 2016 IEEE/ACIS 14th Int. Conf. Softw. Eng. Res. Manag. Appl. SERA 2016, 2016: pp. 45–52. http://doi:10.1109/SERA.2016.7516127.

      [11] Y. Li, C. Tao, Y. Tan, K. Shang, J. Tian, "Unsupervised Multilayer Feature Learning for Satellite Image Scene Classification", IEEE Geosci. Remote Sens. Lett. Vol.13, (2016) pp:157–161. http://doi:10.1109/LGRS.2015.2503142.

      [12] N.M. Salem, Segmentation of white blood cells from microscopic images using K-means clustering, in: Natl. Radio Sci. Conf. NRSC, Proc., 2014: pp. 371–376. http://doi:10.1109/NRSC.2014.6835098.

      [13] M.M. K. Date, "Brain Image Segmentation Algorithm using K-Means Clustering", Int. J. Comput. Sci. Appl. Vol.6, (2013) pp:285–289.

      [14] R.D. Prasad, K.B.V.K. Sai, R.K. Sai, B.V. Manoj, "Content Based Image Retrieval using Color and Texture", Signal Image Process. An Int. J. Vol.3, (2012) pp:39–57. http://doi:10.5121/sipij.2012.3104.

      [15] H. Meng Han, Q.L. Dong, L.B. Lin, P.K. Malakar, "The Potensial of Double K-Means clusteringfor Banana Image Segmentation", J. Food Process Eng. Vol.37, (2014) pp:10–18. http://doi:10.1111/jfpe.12054.

      [16] Z. Qiu-yu, L. Jun-chi, Z. Mo-yi, D. Hong-xiang, L. Lu, "Hand Gesture Segmentation Method Based on YCbCr Color Space and K- Hand Gesture Segmentation Method Based on YCbCr Color Space and K-Means Clustering", Int. J. Signal Process. Image Process. Pattern Recognit. Vol.8, (2015) pp:105–116. http://doi:10.14257/ijsip.2015.8.5.11.

      [17] S.R. Dubey, P. Dixit, N. Singh, J.P. Gupta, "Infected Fruit Part Detection using K-Means Clustering Segmentation Technique", Int. J. Artif. Intell. Interact. Multimed. Vol.2, (2013) pp:65–72. http://doi:10.9781/ijimai.2013.229.

      [18] G. Wang, Y. Zhao, J. Huang, Q. Duan, J. Li, "A K-means-based network partition algorithm for controller placement in software defined network", 2016 IEEE Int. Conf. Commun. ICC 2016. (2016). http://doi:10.1109/ICC.2016.7511441.

      [19] B.F. Solaiman, A. Sheta, "Energy optimization in wireless sensor networks using a hybrid K-means PSO clustering algorithm", Turkish J. Electr. Eng. Comput. Sci. Vol.24, (2016) pp:2679–2695. http://doi:10.3906/elk-1403-293.

      [20] R. Campagni, D. Merlini, R. Sprugnoli, M.C. Verri, "Data mining models for student careers", Expert Syst. Appl. Vol.42, (2015) pp:5508–5521. http://doi:10.1016/j.eswa.2015.02.052.

      [21] D. Kabakchieva, "Predicting student performance by using data mining methods for classification", Cybern. Inf. Technol. Vol.13, (2013) pp:61–72. http://doi:10.2478/cait-2013-0006.

      [22] H.I. Arumawadu, Rathnayaka, R M Kapila Tharanga, S.K. Illangarathne, "Mining Profitability of Telecommunication Customers Using K-Means Clustering", J. Data Anal. Inf. Process. Vol.3, (2015) pp:63–71. http://doi:10.4236/jdaip.2015.33008.

      [23] B.A. Tama, "Data Mining For Predicting Customer Satisfaction in Fast-Food Restaurant", J. Theor. Appl. Inf. Technol. Vol.75, (2015) pp:18–24.

      [24] F. An, H.J. Mattausch, "K-means clustering algorithm for multimedia applications with flexible HW/SW co-design", J. Syst. Archit. Vol.59, (2013) pp:155–164. http://doi:10.1016/j.sysarc.2012.11.004.

      [25] M. Ay, O. Kisi, "Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniques", J. Hydrol. Vol.511, (2014) pp:279–289. http://doi:10.1016/j.jhydrol.2014.01.054.

      [26] S. Kumar, D. Toshniwal, "A data mining approach to characterize road accident locations", J. Mod. Transp. Vol.24, (2016) pp:62–72. http://doi:10.1007/s40534-016-0095-5.

      [27] B. Choubin, K. Solaimani, M. Habibnejad Roshan, A. Malekian, "Watershed classification by remote sensing indices: A fuzzy c-means clustering approach", J. Mt. Sci. Vol.14, (2017) pp:2053–2063. http://doi:10.1007/s11629-017-4357-4.

      [28] M. Yesilbudak, Clustering analysis of multidimensional wind speed data using k-means approach, in: Int. Conf. Renew. Energy Res. Appl., 2016: pp. 961–965. http://doi:10.1109/ICRERA.2016.7884477.

      [29] B. Liu, G. Xu, Q. Xu, N. Zhang, "Outlier Detection Data Mining of Tax Based on Cluster", Phys. Procedia. Vol.33, (2012) pp:1689–1694. http://doi:10.1016/j.phpro.2012.05.272.

      [30] N.R. Ravi, K. Vani, D. Gupta, "Exploration of Fuzzy C Means Clustering Algorithm in External Plagiarism Detection System", Adv. Intell. Syst. Comput. Vol.384, (2016) pp:127–128. http://doi:10.1007/978-3-319-23036-8.

      [31] M. Kim, D.K. Han, H. Ko, "Joint patch clustering-based dictionary learning for multimodal image fusion", Inf. Fusion. (2015) pp:34–36. http://doi:10.1016/j.inffus.2015.03.003.

      [32] J. Agarwal, R. Nagpal, R. Sehgal, "Crime Analysis using K-Means Clustering", Int. J. Comput. Appl. Vol.83, (2013) pp:1–4.

      [33] T. Velmurugan, "Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data", Appl. Soft Comput. J. Vol.19, (2014) pp:134–146. http://doi:10.1016/j.asoc.2014.02.011.

      [34] P. Puri, I. Sharma, "Enhancement in K-mean Clustering to Analyze Software Architecture Using Normalization", Int. J. Sci. Eng. Res. Vol.6, (2015) pp:604–611.

      [35] M. Silic, G. Delac, S. Srbljic, "Prediction of atomic web services reliability based on k-means clustering", Proc. 2013 9th Jt. Meet. Found. Softw. Eng. (2013). http://doi:10.1145/2491411.2491424.


 

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Article ID: 26860
 
DOI: 10.14419/ijet.v7i4.44.26860




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