Clustering with Nature-Inspired Algorithm Based on Territorial Behavior of Predatory Animals
Maciej Trzciński,
Piotr A. Kowalski,
Szymon Łukasik
Affiliations
Maciej Trzciński
Department of Applied Informatics and Computer Physics, Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland
Piotr A. Kowalski
Department of Applied Informatics and Computer Physics, Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland
Szymon Łukasik
Department of Applied Informatics and Computer Physics, Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland
Clustering constitutes a well-known problem of division of unlabelled dataset into disjoint groups of data elements. It can be tackled with standard statistical methods but also with metaheuristics, which offer more flexibility and decent performance. The paper studies the application of the clustering algorithm—inspired by the territorial behaviors of predatory animals—named the Predatory Animals Algorithm (or, in short: PAA). Besides the description of the PAA, the results of its experimental evaluation, with regards to the classic k-means algorithm, are provided. It is concluded that the application of newly-created nature-inspired technique brings very promising outcomes. The discussion of obtained results is followed by areas of possible improvements and plans for further research.
