SciELO - Scientific Electronic Library Online

 
vol.30 issue2Volumetric, Viscosimetric and Thermodynamic study of Alanine in Aqueous solutions of Sodium Sulphate at different TemperaturesAn Approach for Applying Computer Vision in Refrigerator Quality Control on Assembly Lines author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • On index processCited by Google
  • Have no similar articlesSimilars in SciELO
  • On index processSimilars in Google

Share


Información tecnológica

On-line version ISSN 0718-0764

Abstract

PHAM, Trung T.; LOBOS, Gustavo A.  and  VIDAL-SILVA, Cristian L.. Innovation in Data Mining for the Image Processing: K-means Clustering for Data Sets of Elongated Forms and its Application in the Agroindustry. Inf. tecnol. [online]. 2019, vol.30, n.2, pp.135-142. ISSN 0718-0764.  http://dx.doi.org/10.4067/S0718-07642019000200135.

This paper presents an innovative modified method of K-means clustering based on the set theory together with its application in the processing images of the agroindustry field. Traditional K-means permits the clustering of sets in subsets by means of defining their center according to the distance formula. When the data is concentrated in forms without a hyper-spherical sense, this tool allows the center of the set, with a single point, to become a subset of many points. In this article we present a modification of the distance formula that allows giving more flexibility for the study of cases in agriculture. Using numerical examples, the functionality and applicability of the modified method of K-means grouping is evaluated in infrared images from water deficit tests in wheat.

Keywords : K-means clustering; set theory; distance function; environmental stress monitoring.

        · abstract in Spanish     · text in Spanish     · Spanish ( pdf )