Academic Journal of Engineering and Technology Science, 2024, 7(3); doi: 10.25236/AJETS.2024.070325.
Tianzhen Chen1,2
1EIT Data Science and Communication College, Zhejiang Yuexiu University of Foreign Languages, Shaoxing, China
2School of Information Industry, Meizhouwan Vocational Technical College, Putian, China
With the rapid development of Internet of Things (IoT) technology, a vast number of IoT devices continuously generate large and ever-growing datasets, which typically exhibit high-dimensional and dynamically changing characteristics. To effectively manage these data, this study introduces a novel incremental clustering algorithm for arbitrary shaped in IoT data named Arbitrary Shaped Incremental Clustering (ASIC). The ASIC algorithm adapts to ongoing changes in data distributions by updating clustering results in real-time, thus effectively managing dynamic data without the need to reprocess the entire dataset. By comparing with several existing clustering algorithms, this study demonstrates the advantages of ASIC in handling large-scale and dynamic datasets. Experiments conducted on multiple datasets, including AWS IoT, show that the ASIC algorithm excels in clustering quality and operational efficiency. This algorithm is particularly suitable for applications requiring rapid response to environmental changes and real-time updates of data processing results.
Data Mining, IoT Data, Incremental Learning, Clustering Analysis, Data Analysis
Tianzhen Chen. An Incremental Clustering Algorithm for Arbitrary Shaped in IoT Data. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 3: 178-188. https://doi.org/10.25236/AJETS.2024.070325.
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