Clustering Algorithm for IoT Data Stream Based on K-Dimensional Tree and Self-Organizing Density

DOI

10.2991/978-94-6463-447-1_24How to use a DOI?

Keywords

IoT; Discover Knowledge; Data Stream Clustering

Abstract

With the development of IoT technologies, hundreds of millions of devices are constantly generating sensory data streams that contain a wealth of knowledge. To derive interoperable information from them, effective methods and techniques are needed to process and analyze the data streams. The stream clustering techniques in machine learning have gained increasing attention for its ability to rapidly discover knowledge and extract insights from data streams. In this paper, an IoT data stream clustering algorithm based on K-Dimensional tree and Self-Organizing density (KDSO) is proposed. The algorithm creates new clusters using KD trees to reduce the number of redundant clusters and performs range search quickly. In addition, it follows the idea of competitive learning to absorb new data points to facilitate the merging of micro-clusters. Meanwhile, it dynamically adjusts the clustering parameters for micro-cluster update and evolution. Experimental comparisons are made with other advanced methods. The results show that KDSO outperforms the compared methods in terms of clustering purity and silhouette coefficient, and shortens the clustering processing time, proving its good clustering performance.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Daoqu Geng
AU  - Hao Liu
PY  - 2024
DA  - 2024/07/14
TI  - Clustering Algorithm for IoT Data Stream Based on K-Dimensional Tree and Self-Organizing Density
BT  - Proceedings of the 2024 3rd International Conference on Engineering Management and Information Science (EMIS 2024)
PB  - Atlantis Press
SP  - 211
EP  - 218
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-447-1_24
DO  - 10.2991/978-94-6463-447-1_24
ID  - Geng2024
ER  -