Evaluation of Modified K-Means Clustering Algorithm in Crop Prediction

An Agricultural sector is in need for well-organized system to predict and improve the crop over the world. The complexity of predicting the best crops is high due to unavailability of proper knowledge discovery in crop knowledgebase which affects the quality of prediction. In data mining, clustering is a crucial step in mining useful information. The clustering techniques such as k-Means, Expectation Maximization, Hierarchical Micro Clustering, Constrained k-Means, SWK k-Means, k-Means++, improved rough k-Means which make this task complicated due to problems like random selection of initial cluster center and decision of number of clusters. This works demonstrates an evaluation of modified k-Means clustering algorithm in crop prediction. The results and evaluation show comparison of modified k-Means over k-Means and k-Means++ clustering algorithm and modified k-Means has achieved the maximum number of high quality clusters, correct prediction of crop and maximum accuracy count.