An algorithm is a precise, finite sequence of steps or rules designed to solve a problem or perform a computation. Algorithms are the foundation of computer science and underpin everything from sorting and searching to machine learning, data mining, and optimisation. In modelling and data analysis, algorithms are us…
An algorithm is a precise, finite sequence of steps or rules designed to solve a problem or perform a computation. Algorithms are the foundation of computer science and underpin everything from sorting and searching to machine learning, data mining, and optimisation. In modelling and data analysis, algorithms are used to extract patterns from data, classify observations, group similar items, and search large spaces of possible solutions, and their design involves trade-offs between accuracy, efficiency, and interpretability. Model Based Research publishes peer-reviewed studies that develop and apply algorithms across scientific and engineering problems. Reported work includes a study of the ID3 and C4.5 decision-tree learning algorithms, the coupling of genetic algorithms with neural networks to estimate subsurface features of the Earth, the clustering of objects for spatial data mining, and the mining of frequent sequential patterns. Related research applies algorithmic and machine-learning methods to tasks such as detection, prediction, and classification. Together these studies illustrate how algorithmic techniques are formulated, tested, and used to model complex systems and analyse data. This page gathers peer-reviewed, open-access research relevant to algorithms and their application in modelling, data mining, and machine learning.
12 peer-reviewed articles, ranked by relevance. Each links to its DOI.
The 12 articles above have been cited 77 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.
A sample of recent works citing this journal's research on Algorithms, linking to each citing work.