Abstract:
The data mining method was used to deeply analyze
Microcystis aeruginosa experimental simulation data collected from literatures. The results demonstrated that principal component analysis, served as a non-parametric method of classification, could be used to identify the important variables. In addition, the primary factors which influenced the growth of
Microcystis aeruginosa were initial pH (pH
0), alga density (
N0) and total phosphorus (TP
0). The growth of
Microcystis aeruginosa might be inhibited by reducing the value of
N0, pH
0 or TP
0. This indicated that the data mining method could make qualitative analysis for experimental simulation data of
Microcystis aeruginosa.
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