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Erschienen in:
Combining Multiple Features for Product Categorisation by Multiple Kernel Learning Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Enhanced Manhattan-Based Clustering Using Fuzzy C-Means Algorithm

verfasst von : Joven A. Tolentino, Bobby D. Gerardo, Ruji P. Medina

Erschienen in: Recent Advances in Information and Communication Technology 2018

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Decision Fusion Using Fuzzy Dempster-Shafer Theory
Nächstes Kapitel The Cooperation of Candidate Solutions Vortex Search for Numerical Function Optimization
Literatur
1.
Chen, H., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012)
2.
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)CrossRef
3.
Raval, U.R., Jani, C.: Implementing and improvisation of K-Means clustering. Int. J. Comput. Sci. Mob. Comput. 4(11), 72–76 (2015)
4.
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)
5.
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)
6.
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)CrossRef
7.
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)CrossRef
8.
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)
9.
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)
10.
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)
11.
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)
12.
Son, L.H.: Generalized picture distance measure and applications to picture fuzzy clustering. Appl. Soft Comput. J. 46(C), 284–295 (2016)CrossRef
13.
14.
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)
15.
Wang, X., Yu, F., Pedrycz, W.: An area-based shape distance measure of time series. Appl. Soft Comput. J. 46(C), 650–659 (2016)CrossRef
16.
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)
17.
Grover, N.: A study of various fuzzy clustering algorithms. Int. J. Eng. Res. 5013(3), 177–181 (2014)CrossRef
18.
Leddo, J.: Comparing the relative effects of homework on high aptitude VS average aptitude students 2(9), 59–62 (2016)
19.
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)
20.
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)
Metadaten
Titel
Enhanced Manhattan-Based Clustering Using Fuzzy C-Means Algorithm
verfasst von
Joven A. Tolentino
Bobby D. Gerardo
Ruji P. Medina
Copyright-Jahr
2019
Buch
Recent Advances in Information and Communication Technology 2018

Print ISBN: 978-3-319-93691-8

Electronic ISBN: 978-3-319-93692-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-93692-5_13