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.

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