Data Mining Problems Optimization by using Metaheuristic Algorithms: A Survey

Abstract

Big data refers to large, diverse, and complicated data sets that are challenging to store, analyze, and visualize for use in subsequent operations or outcomes. Exploring and analyzing vast amounts of data in order to find significant patterns and principles is called data mining. Data mining is crucial to many human endeavors because it uncovers previously undiscovered patterns that are helpful. There are several main tasks of data mining, including Clustering, feature selection, and association rules. Several data mining techniques are employed to handle these significant duties. Metaheuristic algorithms are currently regarded as one of the most efficient methods for handling data mining issues. Black boxes like metaheuristics can offer distinct solutions regardless of the problem's nature. These algorithms treat data mining problems as combinatorial optimization problems. Numerous research papers are published in this area each year, which is why we decided to give a survey study on the topic. Consequently, this paper provides a thorough literature review on using metaheuristic algorithms to solve data mining issues that have emerged in the last five years (2019-2023).