Diatom Indicating Property Discovery with Rule Induction Algorithm

In the relevant literature the diatoms have ecological preference organized using rule, which takes into account the important influencing physical-chemical parameters on the diatom abundance. Influencing parameters group typically consist from parameters like: conductivity, saturated oxygen, pH, Secchi Disk, Total Phosphorus and etc. In this direction, this paper aims in process of building diatom classification models using two proposed dissimilarity metrics with predictive clustering rules to discover the diatom indicating properties. The proposed metrics play important rule in this direction as it is in every aspects of the estimating quality of the rules, from dispersion to prototype distance and thus lead to increasing the classification descriptive/predictive accuracy. We compare the proposed metrics by classification and rule quality metrics and based on the results, several set of rules for each WQ and TSI category classes are presented, discussed and verified with the known ecological reference found in the diatom literature.