Journal Article10.11591/TELKOMNIKA.V11I12.3570
Multidimensional data mining using a K-mean algorithm based on the forest management inventory of Fujian Province, China
Yanrong Guo,Baoguo Wu,Yang Liu +2 more
TL;DR: In this article, a classification pattern was established using a clustering analysis algorithm and applied to China fir in Fujian Province, where slope position, elevation, elevation and humus depth were important factors affecting the stand volumes of young/immature forests.
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Abstract: To determine relationships between stand volume and site factors in the absence of information about stand age and density, a classification pattern was established using a clustering analysis algorithm and applied to China fir in Fujian Province. The results showed that slope position, elevation, elevation and humus depth were important factors affecting the stand volumes of young/immature forests, near-mature forests, and mature/overmature forests, respectively. The K-mean algorithm could be used to evaluate the influences of site factors on stand volume under different stand age groups and density conditions. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3570
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Application of Parallel Annealing Particle Clustering Algorithm in Data Mining
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Modelling Data Mining Dynamic Code Attributes with Scheme Definition Technique
Evasaria M Sipayung,Cut Fiarni,Randy Tanudjaja +2 more
TL;DR: This study proposes a system to extract attributes from complex data, determine product prices based on hidden relationships, achieving 98.7% precision and 70.27% recall rates, enhancing data preparation for data mining algorithms.
References
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Anil K. Jain
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Data Clustering: 50 Years Beyond K-means
Anil K. Jain
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TL;DR: Cluster analysis as mentioned in this paper is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics, which is one of the most fundamental modes of understanding and learning.