Abstract
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.
Highlights
Unsupervised learning corresponds to the task of extracting useful information from unlabelled data. It does not assume any prior knowledge, and it is usually associated with the problems of clustering and outlier detection [2]
The aim of this paper is to provide a new method of clustering based on natural inspiration
We demonstrate here that the proposed approach can bring high quality of clustering solutions, especially for multidimensional problems, with multiple clusters potentially present in the data
Summary
The past few years have brought the increasing role of data science and machine learning as universal research domains allowing to get valuable insights from data coming from a variety of fields. Learning paradigms can be classified as supervised and unsupervised. The supervised learning model assumes the availability of the information on the class membership of each training instance [1]. Unsupervised learning corresponds to the task of extracting useful information from unlabelled data. It does not assume any prior knowledge, and it is usually associated with the problems of clustering and outlier detection [2]. Clustering or cluster analysis corresponds to the task of data division into coherent structures, named clusters, which are grouping similar data elements
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