A Comprehensive Overview Of Data Mining Algorithms

Abstract

 Since processing and storage capacity have increased over the past 10 years, we are now able to store this tremendous quantity of data. To gain information, this data is being analyzed and stored. It is standard practice to use classification algorithms to extract information from publically accessible data. Several of the most well-liked data mining classification techniques are studied in this article. In classification processes, statistical, machine learning, or neural network techniques are often utilized. This study gives a detailed analysis of several classification algorithms, along with their benefits and drawbacks, while taking these techniques into consideration. Data mining is the process of extracting useful information from databases. Another term for it is knowledge discovery. Using a mix of machine learning, statistical analysis, modelling techniques, and database technology, data mining uncovers patterns and subtle correlations in data and deduces principles that enable future prediction. The incorporation of time-series, clustering, association rules, decision trees, and other data mining activities. It outlines the algorithms' operation and the data they need. Each algorithm has its own set of advantages and disadvantages. How quickly insights can be drawn from data often determines success and improved decision-making. These revelations may be used to anticipate conduct in the future, enhance operational processes, and even inspire better behaviour. This document provides a summary of the several methods necessary to manage massive data gathering.