Open AccessClustering the Vegetation Areas using Fuzzy CMeans Algorithm
Author(s) -
N. Saranya,
N. Kanthimathi,
A. Shyamalaprasanna,
S. Vidhya,
S. Dharani
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c5493.029320
Subject(s) - cluster analysis , focus (optics) , vegetation (pathology) , land cover , computer science , cluster (spacecraft) , fuzzy logic , fuzzy clustering , k means clustering , data mining , pattern recognition (psychology) , algorithm , geography , artificial intelligence , land use , ecology , medicine , physics , pathology , optics , biology , programming language
To detect the vegetation land from google earth image and clustered that vegetation land to get the different clusters and so the area of clustered land is calculated. The detection is done by land cover classification usingafuzzy Cmeans clustering because it overcomes the disadvantage of Kmeans clustering algorithm because that clustered land is based on the land attributes not a particular distance.The exhibition of the FCM algorithm relies upon the choice of the primary cluster focus and the primary enrollment esteem. On the off chance that best primary cluster focus that is near the real, last cluster focus can be discovered, the FCM algorithm quickly cover the particular area and the preparing time can be radically decreased. Which altogether diminishes the calculation time required to segment a dataset into desired clusters?