Open Access   Article

Different Query Optimization Techniques (QOT) using Data Mining Technology

I. Shahina Begam1 , K. Tajudin2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 1479-1487, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i6.14791487

Online published on Jun 30, 2018

Copyright © I. Shahina Begam, K. Tajudin . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: I. Shahina Begam, K. Tajudin, “Different Query Optimization Techniques (QOT) using Data Mining Technology”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1479-1487, 2018.

MLA Style Citation: I. Shahina Begam, K. Tajudin "Different Query Optimization Techniques (QOT) using Data Mining Technology." International Journal of Computer Sciences and Engineering 6.6 (2018): 1479-1487.

APA Style Citation: I. Shahina Begam, K. Tajudin, (2018). Different Query Optimization Techniques (QOT) using Data Mining Technology. International Journal of Computer Sciences and Engineering, 6(6), 1479-1487.

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Abstract

Data mining is one of the main research areas to find particular data from the large set of data. The main aim of this paper is to give more knowledge about the agriculture sector. Agriculture is one of the main economic parts of growing country. Agricultural statistical data from India as been taken here the state of Kerala. To cover Kerala state 14 districts cropped data analysis, in the 12 years pattern of statistical dataset, start from 2005 to 2017. Utilization of Query Optimization Techniques (QOT), K-Means clustering and Filter Techniques (FT). The QOT analysation is to provide variety of query generation and reports. The K-Means clustering, is usage of spatio-temporal cluster data mining techniques. It also provides changes report of the dataset. Using clustering analysis is the process of discovering groups. The FT is used to filter the data season wise and found maximum production of rice occurrence district report.

Key-Words / Index Term

Cropped area-Grouping-Query optimization-Maximum Production

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