A Parallel Forecasting Approach Using Incremental K-means Clustering Technique

Abstract

A parallel forecasting approach used in weather prediction which has important aspect of the modern society, especially with the realization of modern smart cities. A new approach is considering here which will provide excellent result for large semi-structured data. Thus, it is very important to analyze weather data keeping in mind the enormity of the available data sizes. Here it is presented a methodology for weather data analysis keeping in mind the big-data nature of the data sizes pertaining to weather data. Here also it has been taken date-wise atmospheric conditions collected of decade. The traditional k-means clustering is used to form clusters which represents association in between related dates of current year’s and previous year’s weather data. Such associations predict atmospheric conditions of one year’s weather condition on the bases of previous data. Incremental k-means clustering algorithm is used to process current year’s weather parameters as new data and it shows that the calculated weather condition falls under one of the existing clusters to represent similar atmospheric conditions. The total work has been divided by two parts: first, Storing NCDC semi-structured data on hadoop cluster and second, fitting a clustering methodology for predicting weather conditions.