Learning diatom ecological models with fuzzy order data mining algorithm

Abstract

The data mining algorithms allow data scientist to extract useful knowledge from raw measured data. The fuzzy data mining algorithms have several advantages over crisp methods, and they have been used more often to obtain ecological knowledge from ecological data. In this paper, we aim to learn suitable habitat models of the ecological conditions where diatoms can exist in lake ecosystems by using a fuzzy data mining algorithm. The algorithm uses several different fuzzy concepts, namely, fuzzy membership functions, similarity metrics and order weighted geometric operator to build predictive ecological model that is able to reveal patterns in ecological data and thus find the suitable diatom ecological conditions. Additionally, we have made experimental evaluation of two similarity metrics that influence the accuracy for both descriptive and predictive models. Later, the results of the models are verified with the known ecological preferences found in the literature. Based on the obtained results, in future we plan to improve the fuzzy operators and similarity metrics and test other membership functions on new ecological data.