Department Computing Technology, School of Computing, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, 603203, Tamil Nadu, India.
From smart home devices to wearable devices, electronics have become an indispensable part of modern life. Vast volumes of data have been collected by these electronic devices, revealing precise information about device communications, user behaviours, and more. Improvements to device features, insights into the user experience, and the detection of security risks are just some of the many uses for this information. However, advanced analytical methods are required to make sense of this plethora of data successfully. The K-means clustering algorithm is used in the present research to analyse the data sent and received by different types of electronics. The first step of the research is collecting data, intending to create a representative sample of people using various devices and communication methods. After collecting data, preprocessing is necessary to ensure it can be analysed successfully. In the next step, the K-means algorithm classifies the information into subsets that stand for distinct modes of interaction. The primary objective of the research is to gain an improved understanding of these groups by demonstrating how users communicate, device communication, and possibilities for enhancing functionality and security.
Keywords
Communication Data, K-Means, Clustering, User Behavior, Electronic Devices, Security,
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Kalaivani S
Kalaivani S
Department of ECE, B.S.A Crescent Institute of Science and Technology, Chennai, 600048. Tamil Nadu, India.
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Cite this article
Leonid Alemán Gonzales, Kalaivani S, Saranya S S, Anto Bennet M, Srinivasarao B and Alhi Jordan Herrera Osorio, “Harnessing K-means Clustering To Decode Communication Patterns In Modern Electronic Devices”, Journal of Machine and Computing, pp. 031-039, January 2024. doi: 10.53759/7669/jmc202404004.
