Efficiency Analysis of Hybrid Fuzzy C-Means Clustering Algorithms and their Application to Compute the Severity of Disease in Plant Leaves
Journal Title: Computer Reviews Journal - Year 2019, Vol 3, Issue 0
Abstract
Data clustering has a wide range of application varying from medical image analysis, social network analysis, market segmentation, search engines, recommender systems and image processing. A clustering algorithm should be fast as well accurate. Some applications give priority to the speed of the clustering algorithms while some emphasize more on the accuracy rather than speed. A number of clustering algorithms have been proposed in the literature. Some of these include Fuzzy C-Means (FCM), Intuitionistic Fuzzy C-Means (IFCM), Rough Fuzzy C-Means (RFCM) and Rough Intuitionistic Fuzzy C-Means (RIFCM). In this paper, we compare the accuracy and execution time of the fuzzy based clustering algorithms. The clustering algorithms are applied on an image dataset and their running time as well as accuracy is compared by varying the number of clusters. Our results show that there is a clear trade-off between execution time and accuracy of these clustering algorithms. Also, we apply these algorithms on two different diseased leaf images and compute the severity of the disease of the leaves.
Authors and Affiliations
Anmol Agrawal, B. K. Tripathy
Keywords
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