Data Mining Algorithms for IoT: A Succinct Study
Chandrakanta Mahanty, Brojo Kishore Mishra, Raghvendra Kumar · Apple Academic Press eBooks · 2022
IoT is a fresh idea which enables people to connect different sensors and intelligent machines to gather information from the surroundings in real time. It is anticipated that the number of effective IoT machines will increase to 0.01 trillion and 0.022 trillion by 2020 and 2025, respectively. Lots of analytical techniques are brought into IoT to render IoT smarter; data mining is among the most precious techniques. Data mining is the method of finding interesting knowledge and possibly relevant patterns from big data sets and using algorithms to extract crucial information. This article focuses on data mining framework for IoT, data mining functionalities and usage of data mining in IoT applications. After that, a survey on different data mining algorithms is presented. We also analyze the effectiveness and efficiency of different data mining algorithms (K-nearest neighbors, Naïve Bayes, support vector machine (SVM), C5.0, deep learning artificial neural networks (ANN), and ANNs). We reviewed the above mentioned algorithms and concluded that DLANNs, ANNs, C5.0 give relatively higher accuracy and memory-efficient as compared to other algorithms. To address IoT data mining problems such as managing large quantities of information, data analysis (DA), a big data mining system is suggested.