Experimental Study of Data Mining Classification Algorithms in Establishing Indian Agricultural Commodity Patterns

Abstract

This paper presents novel idea of how to establish various products pattern of Indian agricultural commodity using Data Mining Classification Algorithms. Generally when we talk about Data Mining we come across several basics and advance technique’s to incorporate for the broader applications of usage. Now we want to use Data Mining algorithms to extract some very interesting patterns by detailed study of agricultural data sets. As we all know computing and information has vast scope to deal for commercial usage but picture changes when it comes to medium profitable segments. In this paper I have tried experimental basis of Data Sets using agricultural products category by extracting from local APMC (Agricultural Product Market Cooperation). This organization helps locally to guide farmers to know the best price for selling and buying. If we incorporate new trend setting solution that makes more transparent and predictable solution patterns for farmers of local community and compare the price segments with national monitoring markets like Agmark(Agricultural market of India). The research establishes various classifications based on given class of market by using Naïve Bayes and Bayes Net algorithms and comparing with Rules one R [1R] and Trees.J48.

Keywords: Data Mining, Classification, Algorithms, Knowledge and Data Engineering Tools and Techniques, Agricultural Products, Agricultural Markets

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