Hybrid K Mean Clustering Algorithm for Crop Production Analysis in Agriculture
Vandana B1, S Sathish Kumar2

1Vandana B, Research Scholar, Department of Computer Science and Engineering, RNSIT, Visvesvaraya Technological University, Belagavi (Karnataka), India.

2Dr. S Sathish Kumar, Professor, Department of ISE, RNSIT, VTU, Bengaluru (Karnataka), India.

Manuscript received on 03 December 2019 | Revised Manuscript received on 11 December 2019 | Manuscript Published on 31 December 2019 | PP: 9-13 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10021292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1002.1292S19

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The proposed research work aims to perform the cluster analysis in the field of Precision Agriculture. The k-means technique is implemented to cluster the agriculture data. Selecting K value plays a major role in k-mean algorithm. Different techniques are used to identify the number of cluster value (k-value). Identification of suitable initial centroid has an important role in k-means algorithm. In general it will be selected randomly. In the proposed work to get the stability in the result Hybrid K-Mean clustering is used to identify the initial centroids. Since initial cluster centers are well defined Hybrid K-Means acts as a stable clustering technique.

Keywords: Cluster Analysis, K-Means, Precision Agriculture.
Scope of the Article: Algorithm Engineering