Version 1
: Received: 5 September 2024 / Approved: 5 September 2024 / Online: 6 September 2024 (08:08:07 CEST)
How to cite:
DEMİR, M. A New Meta-Heuristic Method: Pi Algorithm and An Its Application on Data Clustering. Preprints2024, 2024090478. https://doi.org/10.20944/preprints202409.0478.v1
DEMİR, M. A New Meta-Heuristic Method: Pi Algorithm and An Its Application on Data Clustering. Preprints 2024, 2024090478. https://doi.org/10.20944/preprints202409.0478.v1
DEMİR, M. A New Meta-Heuristic Method: Pi Algorithm and An Its Application on Data Clustering. Preprints2024, 2024090478. https://doi.org/10.20944/preprints202409.0478.v1
APA Style
DEMİR, M. (2024). A New Meta-Heuristic Method: Pi Algorithm and An Its Application on Data Clustering. Preprints. https://doi.org/10.20944/preprints202409.0478.v1
Chicago/Turabian Style
DEMİR, M. 2024 "A New Meta-Heuristic Method: Pi Algorithm and An Its Application on Data Clustering" Preprints. https://doi.org/10.20944/preprints202409.0478.v1
Abstract
Meta-heuristic algorithms are methods that try to solve problems by imitating events and systems existing in nature. It can be very difficult to solve nonlinear problems with the help of known classical algorithms. In this study, a new meta-heuristic method, the Pi algorithm, is proposed. Pi is a special number that is not periodic and contains many different number combinations in its digits calculated so far. The most important part of meta-heuristic methods is the equations and parameters used to ensure the diversity of the created population. In this study, a specific Pi coefficient for each attribute was calculated with the help of the Monte-Carlo method. These coefficients were used in the Pi algorithm in other parts of the study. In the application for data clustering, experiments were made on 5 different data sets. The best accuracy rate of 95.5% was achieved.
In this respect, it has been seen that the Pi algorithm works successfully as a meta-heuristic method. It can be studied on a population-based data clustering, classification, prediction, etc. It can be easily applied in all areas.
Keywords
Monte-Carlo method; pi number; heuristic; clustering
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
