Analysis of Microcystis aeruginosa Experimental Simulation Data Based on Data Mining

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 (pH₀), alga density (N₀) and total phosphorus (TP₀). The growth of Microcystis aeruginosa might be inhibited by reducing the value of N₀, pH₀ or TP₀. This indicated that the data mining method could make qualitative analysis for experimental simulation data of Microcystis aeruginosa.