"基于Hadoop的并行小波聚类算法在大数据处理中的应用研究"

Abstract The rapid development of the internet has led to the generation of massive amounts of data. Handling such a vast volume of data using a single technology is challenging, and as a result, parallel technology has become the preferred approach for data processing. This research paper focuses on the design of a parallel wavelet clustering algorithm, leveraging the localization analysis of wavelets in both time and frequency domains. The wavelet clustering algorithm has demonstrated strong performance in data processing, making it a promising candidate for dealing with large datasets. This paper explores the use of Hadoop, a popular parallel processing framework, to implement the parallel wavelet clustering algorithm. By distributing the computational workload across multiple nodes, Hadoop enables efficient processing of big data. The study aims to demonstrate the effectiveness of the proposed parallel wavelet clustering algorithm using Hadoop through experimental evaluation. The findings of this research have the potential to contribute to the advancement of parallel computing techniques for data clustering and processing.