Abstract
In the process of clustering, our attention is to find out basic procedures that measures the degree of association between the variables. Many clustering methods use distance measures to find similarity or dissimilarity between any pair of objects. The fuzzy c-means clustering algorithm is one of the most widely used clustering techniques which uses Euclidean distance metrics as a similarity measurement. The choice of distance metrics should differ with the data and how the measure of their comparison is done. The main objective of this paper is to present mathematical description of different distance metrics which can be acquired with different clustering algorithm and comparing their performance using the number of iterations used in computing the objective function, the misclassification of the datum in the cluster, and error between ideal cluster center location and observed center location.
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Arora, J., Khatter, K., Tushir, M. (2019). Fuzzy c-Means Clustering Strategies: A Review of Distance Measures. In: Hoda, M., Chauhan, N., Quadri, S., Srivastava, P. (eds) Software Engineering. Advances in Intelligent Systems and Computing, vol 731. Springer, Singapore. https://doi.org/10.1007/978-981-10-8848-3_15
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DOI: https://doi.org/10.1007/978-981-10-8848-3_15
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