Abstract
Fuzzy C-Means is a clustering algorithm known to suffer from slow processing time. One factor affecting this algorithm is on the selection of appropriate distance measure. While this drawback was addressed with the use of the Manhattan distance measure, this sacrifice its accuracy over processing time. In this study, a new approach to distance measurement is explored to answer both the speed and accuracy issues of Fuzzy C-Means incorporating trigonometric functions to Manhattan distance calculation. Upon application of the new approach for clustering of the Iris dataset, processing time was reduced by three iterations over the use of Euclidean distance. Improvement in accuracy was also observed with 50% and 78% improvement over the use of Euclidean and Manhattan distances respectively. The results provide clear proof that the new distance measurement approach was able to address both the slow processing time and accuracy problems associated with Fuzzy C-Means clustering algorithm.
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References
Chen, H., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012)
Jung, Y.G., Kang, M.S., Heo, J.: Clustering performance comparison using K-Means and expectation maximization algorithms. Biotechnol. Biotechnol. Equip. 28(sub1), 44–48 (2014)
Raval, U.R., Jani, C.: Implementing and improvisation of K-Means clustering. Int. J. Comput. Sci. Mob. Comput. 4(11), 72–76 (2015)
Han, D., Ji, J., Dai, Y., Li, G., Fan, W., Chen, H.: Improved Fuzzy C-Means algorithm and its application to classification of remote sensing image in Chengdu City. In: 2016 International Conference on Progress in Informatics and Computing (PIC), pp. 437–443. IEEE, Shanghai (2016)
Cebeci, Z., Yildiz, F.: Comparison of K-Means and Fuzzy C-Means algorithms on different cluster structures. J. Agric. Inf. 6(3), 13–23 (2015)
Winkler, R., Klawonn, F., Kruse, R.: Problems of Fuzzy C-Means clustering and similar algorithms with high dimensional data sets. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds.) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 1–8. Springer, Heidelberg (2012)
Fahad, A., et al.: A survey of clustering algorithms for big data : taxonomy & empirical analysis. IEEE Trans. Emerg. Topics Comput. 2(3), 267–279 (2014)
Sharma, S.K., Kumar, S.: Comparative analysis of Manhattan and Euclidean distance metrics using A* algorithm. J. Res. Eng. Appl. Sci. 1(4), 196–198 (2016)
Kalaivani, E.R., Suganya, G., Kiruba, J.: Review on K-Means and fuzzy C means clustering algorithm. Imp. J. Interdiscip. Res. 3(2), 1822–1824 (2017)
Kouser, K.: A comparative study of K means algorithm by different distance measures. Int. J. Innov. Res. Comput. Commun. Eng. 1(9), 2443–2447 (2013)
Kapoor, A.: A comparative study of K-Means, K-Means ++ and Fuzzy C-Means clustering algorithms. In: 3rd International Conference on Computational Intelligence & Communication Technology (CICT), pp. 1–6. IEEE (2017)
Son, L.H.: Generalized picture distance measure and applications to picture fuzzy clustering. Appl. Soft Comput. J. 46(C), 284–295 (2016)
Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420–434. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44503-X_27
Rathod, A.B.: A comparative study on distance measuring approaches for permutation representations. In: 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), pp. 251–255 (2016)
Wang, X., Yu, F., Pedrycz, W.: An area-based shape distance measure of time series. Appl. Soft Comput. J. 46(C), 650–659 (2016)
Wang, Z., Zhao, N., Wang, W., Tang, R., Li, S.: A fault diagnosis approach for gas turbine exhaust gas temperature based on Fuzzy C-Means clustering and support vector machine. Math. Prob. Eng. 2015, 1–11 (2015)
Grover, N.: A study of various fuzzy clustering algorithms. Int. J. Eng. Res. 5013(3), 177–181 (2014)
Leddo, J.: Comparing the relative effects of homework on high aptitude VS average aptitude students 2(9), 59–62 (2016)
Dom, B.E., Jose, S.: An information-theoretic external cluster-validity measure. In: Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI 2002), pp. 137–145, Alberta, Canada (2002)
Färber, I., et al.: On using class-labels in evaluation of clusterings. In: Proceedings of 1st International Workshop on Discovering, Summarizing and Using Multiple Clusterings (MultiClust 2010) Held in Conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), p. 9. Washington, D.C., USA (2010)
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This study would not be possible without the financial support of the Commission on Higher Education Kto12 Transition Program Unit Quezon City, Philippines and Tarlac Agricultural University Tarlac, Philippines.
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Tolentino, J.A., Gerardo, B.D., Medina, R.P. (2019). Enhanced Manhattan-Based Clustering Using Fuzzy C-Means Algorithm. In: Unger, H., Sodsee, S., Meesad, P. (eds) Recent Advances in Information and Communication Technology 2018. IC2IT 2018. Advances in Intelligent Systems and Computing, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-93692-5_13
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DOI: https://doi.org/10.1007/978-3-319-93692-5_13
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