Improve K-Means algorithm to increase the accuracy of data classification in the internet of Things using ACO and PSO algorithms

Abstract

This study aims to analyze the data which received in the community as much as possible given the growing prevalence of the Internet of Things. It was essential to employ practical and effective methodologies. It acted truly deals with the Internet-based interconnection and communication of various items. The IoT's biggest problem was managing the data sent to it because of the diversity and volume of data that it received. Utilizing data mining and its numerous algorithms, including information clustering, was one of the most effective ways to handle this problem. Information clustering was one of the most useful techniques for categorizing IoT data. One of the key data mining techniques wais The Kmeans algorithm, which had drawbacks if additional algorithms were offered, was one of the most crucial ways for data clustering, a task for which several approaches had been presented. In the field of data mining, which, in addition to the use of IoT data mining, would be beneficial in other areas connected to data mining, might be of great assistance in resolving or minimizing its issues. Additionally, the outcomes of using the suggested technique and evaluating it against existing algorithms to improve clustering performance are shown.