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Measuring, archetyping and mining Olea europaea production data

Measuring, archetyping and mining Olea europaea production data Purpose – A huge amount of data are produced in the agriculture sector. Due to the huge number of these datasets it is necessary to use data analysis techniques in order to comprehend the data and extract useful information. The purpose of this paper is to measure, archetype and mine olea europaea production data. Design/methodology/approach – This work applies three different data mining techniques to data about Olea europaea var. media oblonga from the island of Thassos, at the northern part of Greece. The data were from 1,063 farmers from three different municipalities of Thassos, namely Kallirachi, Limenaria and Prinos and concerned the year 2010. They were analysed using the classification algorithm OneR, the clustering algorithm k‐means and the association rule mining algorithm, Apriori from the WEKA data mining package. Also, new measures which quantify the performance of the productions of olives and oil are applied. Finally, archetypal analysis is applied in order to distinguish the most typical/stereotype farms for each region and describe their specific characteristics. Findings – The results indicate that organic cultivation could improve the production of olives and olive oil. Furthermore, the climate differences among the three municipalities seems to be a factor involved in production efficacy. Originality/value – It is the first time that data from the island of Thassos have been analysed systematically using a variety of data mining methods. Also, the measures proposed in the paper in order to analyse the data are new. Furthermore, archetypal analysis is proposed as a method to extract sterotypes/representative farms from the dataset. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Systems and Information Technology Emerald Publishing

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Publisher
Emerald Publishing
Copyright
Copyright © 2012 Emerald Group Publishing Limited. All rights reserved.
ISSN
1328-7265
DOI
10.1108/13287261211279062
Publisher site
See Article on Publisher Site

Abstract

Purpose – A huge amount of data are produced in the agriculture sector. Due to the huge number of these datasets it is necessary to use data analysis techniques in order to comprehend the data and extract useful information. The purpose of this paper is to measure, archetype and mine olea europaea production data. Design/methodology/approach – This work applies three different data mining techniques to data about Olea europaea var. media oblonga from the island of Thassos, at the northern part of Greece. The data were from 1,063 farmers from three different municipalities of Thassos, namely Kallirachi, Limenaria and Prinos and concerned the year 2010. They were analysed using the classification algorithm OneR, the clustering algorithm k‐means and the association rule mining algorithm, Apriori from the WEKA data mining package. Also, new measures which quantify the performance of the productions of olives and oil are applied. Finally, archetypal analysis is applied in order to distinguish the most typical/stereotype farms for each region and describe their specific characteristics. Findings – The results indicate that organic cultivation could improve the production of olives and olive oil. Furthermore, the climate differences among the three municipalities seems to be a factor involved in production efficacy. Originality/value – It is the first time that data from the island of Thassos have been analysed systematically using a variety of data mining methods. Also, the measures proposed in the paper in order to analyse the data are new. Furthermore, archetypal analysis is proposed as a method to extract sterotypes/representative farms from the dataset.

Journal

Journal of Systems and Information TechnologyEmerald Publishing

Published: Nov 9, 2012

Keywords: Greece; Agriculture; Trees; Data management; Data mining; Data analysis; Olea europaea; Measures; Archetypal analysis; Classification; Clustering; Association rule mining

References