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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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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