Conglomerate Crop Recommendation by using Multi-label Learning via Ensemble Supervised Clustering Techniques

Abstract

Existing crop recommender related to either binary classification or multiclass classification. This paper presents conglomerate crop recommendations which consist of a number of different and distinct crops that are grouped together. In this work we focus on transferring knowledge from single label output prediction to multiple label predicted output for a given input data instances. We proposed ESCT algorithm i.e. Ensemble Supervised Clustering Techniques in our research work. ESCT provides a combined approach of conventional clustering and enhanced supervised clustering methodology to optimize the conglomerate recommendation. We are focusing on K-mean clustering for conventional approach and ICCC i.e. Inter cluster correlation coefficient to achieve enhancement in supervised clustering. In conventional K-mean clustering there is a big challenge on how to optimize the k-value of clustering which directly affects the convergence of the clusters. To resolve this problem, we mainly apply function approximation on K-Value which provides us with better clustering and fast convergence. Existing methods for inter-clustering do not adequately address one of the key challenges i.e. exploiting correlations between labels and that is achieved by ICCC algorithm. This model provides learning and prediction of unknown observation by using Back propagation MLL algorithm which provides improved performance.