International Journal of Data Mining and Emerging Technologies

  • Year: 2016
  • Volume: 6
  • Issue: 2

Classification and Cross Validation of Agricultural Productivity Performance of Tamilnadu Using Neural Network Approach

1Assistant Professor, Department of Statistics, DRBCCC Hindu College, Pattabiram, Chennai-72, Tamil Nadu, India

2Assistant Professor, Department of Statistics, Vivekanandha College of Arts and Sciences for Women, Namakkal, Tiruchengode-637205, Tamil Nadu, India

3Assistant Professor, Department of Statistics, Dr. Ambedkar Govt. Arts College, Vyasarpadi, Chennai-600039, Tamil Nadu

*(Corresponding author) email id: manimannang@gmail.com

**arulstat80@gmail.com

***priyagayu2006@gmail.com

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Abstract

This study is based on estimation of CAPI (Composite Agriculture Productivity Indices) and to identify the performance of CAPI using k-means clustering algorithm. In addition, to cross validate the results of k-means clustering technique, artificial neural network is also used and finally graded the agriculture productivity performance of various districts of Tamilnadu. The grades are high performance productivity, moderate performance productivity and low performance productivity during the study period 2002–2003 to 2011 — 2012[11]. The secondary source of database consists four major categories, namely cereals, pulses, oilseeds and cash crops The yield from 28 districts of 10 years tenure were collected from the Department of Economics and Statistics, Chennai, Tamilnadu.

Keywords

CAPI, k-Mean clustering techniques, Artificial neural network, Data mining, Scaled conjugate gradient (SCG) algorithm, Crops and Classification