Multidimensional Data Mining using a K-mean Algorithm based on the Forest Management Inventory of Fujian Province, China

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.