Implementation and comparison of K-means and fuzzy C-means algorithms for agricultural data

Clustering is an unsupervised technique is used for organizing the data for efficient retrieval. This is mainly used in pattern reorganization and data analysis. Today many cluster analysis techniques are used for data analysis and have proven to be very useful in segmentation. Performance of these algorithms is data dependent. In this paper K-Means and Fuzzy C-Means are implemented for segmenting the agricultural diseased data. The proposed research work compares the computing performance and clustering accuracy of K-Means clustering with FCM clustering algorithm. Experimental results showed that higher performance is achieved by K-Means clustering when compared with FCM.