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Big Data Clustering Analysis Algorithm for Internet of Things Based on K-Means

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  • Zhanqiu Yu

    (Anhui Technical College of Industry and Economy, Hefei, China)

Abstract

To explore the Internet of things logistics system application, an Internet of things big data clustering analysis algorithm based on K-mans was discussed. First of all, according to the complex event relation and processing technology, the big data processing of Internet of things was transformed into the extraction and analysis of complex relational schema, so as to provide support for simplifying the processing complexity of big data in Internet of things (IOT). The traditional K-means algorithm was optimized and improved to make it fit the demand of big data RFID data network. Based on Hadoop cloud cluster platform, a K-means cluster analysis was achieved. In addition, based on the traditional clustering algorithm, a center point selection technology suitable for RFID IOT data clustering was selected. The results showed that the clustering efficiency was improved to some extent. As a result, an RFID Internet of things clustering analysis prototype system is designed and realized, which further tests the feasibility.

Suggested Citation

  • Zhanqiu Yu, 2019. "Big Data Clustering Analysis Algorithm for Internet of Things Based on K-Means," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 10(1), pages 1-12, January.
    • Handle: RePEc:igg:jdst00:v:10:y:2019:i:1:p:1-12
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