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Knowledge Discovery and Data Mining to Identify Agricultural Patterns.

Journal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.3, No. 3)

Publication Date:

Authors : ; ;

Page : 1337-1345

Keywords : KDD(Knowledge Discovery in Database Process); WEKA(Waikato Environment for Knowledge Analysis); CART(Classification and Regression tree); DM(Data Mining); CRM(Customer Relationship Management);

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With the evolution of computer based data storage systems we have come across a huge amount of repository of data. But this data is not very helpful until we know what we can do with it. We need to make inferences from this immense data so that we can make decisions driven by knowledge. Data mining is the process of knowledge discovery in database. Mining the agricultural patterns is one of its applications. From last few decades data mining in agriculture is recent research area. Till now data mining techniques were used in the businesses and corporate sectors, but now these techniques are also being used for extraction of efficacious agricultural data. With the help of KDD and data mining we extract the meaningful data sets from the gigantic amount of data. The k-means clustering is used to classify the given set of data. This technique when applied on the large set of data then it results into improved quality of mined data. We have applied this method to study the production and consumption of crops in various parts of India. The various factors which affect the production of crops like soil type and weather are taken into consideration. For graphically representation we have used spatial join with the algorithm

Last modified: 2014-05-24 19:55:44