Applications of Clustering Algorithms for Analysis of Agriculture Data for Different Districts in Karnataka

Abstract

Data Mining Algorithms plays a significant role for the data analysis in many disciplines like Agriculture, Food, Medicine, Twitter Data etc. Data Clustering Algorithms are falls under the unsupervised classification category. Clustering is a very challenging task because of little or no prior knowledge. Data Clustering Algorithms are essential approaches to analyse the agricultural data and also to achieve the practical and effective solutions for agricultural problems such as suitable crop for the particular soil type, crop which can produce maximum production the environments like more temperature, less rain fall, less nitrogen content in the soil etc. Variations in the environmental conditions like sudden raises in temperature, reduced / increased rainfall, variations in the market prices, etc with all these aspects it is difficult for formers to take critical farming decisions. The environmental parameters like temperature, Rainfall, Humidity, season (kharif, rabi, summer) and soil parameters of different states of Karnataka are analysed in this paper for maximizing the Rice and Wheat crop production using data clustering methods like DBSCAN and CLIQUE. The Cluster Quality metrics used to measure the well posed structure and goodness of the clusters formed by different clustering algorithms. Few of the external quality metrics parameters are Completeness, Purity, Homogeneity and V-measure.