Information Visualization Using Clustering and Predictive Model

Abstract

This paper aims to reveal the impact of rainfall on tea export from India, an issue that remained unexplored in the existing literature. This study explores a new model to predict India's tea export more accurately that would be helpful for Indian tea planters and exporters to plan their production as well as the inventory holding for deriving maximum value from tea export. A two-stage modelling approach has been developed. Firstly, an artificial intelligence-based growing hierarchical self-organising map algorithm is employed on the monthly time series of monthly frequency spreading over April 2005 to December 2013 to segregate India's monthly tea export data series into visual clusters of recognized pattern. Further, a predictive model using support vector machine has been developed and applied to forecast the tea export and then the importance of the predictor variables of the tea export have been identified. Finally, using the appropriate measures of performance a comparative analysis has been performed for each of the model. The newness of the study pertains to the two facts revealed from the study: firstly, India's tea export is embedded of complexity and nonlinearity, which could receive a successful clustering through growing hierarchical self organizing map that would make a deeper analysis easier with a further application of rich statistical techniques. Secondly, the analysis of prediction errors and the relative importance of the predictor variables establish rainfall as one of the most significant variable in predicting India's tea export, insight that has never surfaced in the literature developed thus far.