"基于密度的K-Medoids聚类算法在Hadoop平台下的研究与实现"

With the rapid development of Internet technology, the amount of data available to individuals and organizations has seen explosive growth. Traditional data mining algorithms are often unable to efficiently handle such large volumes of data, leading to a need for more efficient and scalable solutions. In this context, the K-Medoids clustering algorithm has emerged as a classic method for clustering data into distinct groups. To address the challenge of processing large datasets, this paper explores the implementation of the K-Medoids algorithm on the Hadoop platform. By leveraging the distributed computing capabilities of Hadoop, the proposed parallel K-Medoids algorithm is able to significantly improve the efficiency of clustering large datasets. The key innovation of this research lies in the incorporation of density-based clustering techniques into the K-Medoids algorithm. By taking into account the density of data points in the clustering process, the algorithm is able to identify clusters of varying shapes and sizes, making it more robust and adaptable to real-world datasets. Through a series of experiments and performance evaluations, the effectiveness of the proposed algorithm is demonstrated in terms of both accuracy and efficiency. The results show that the parallel K-Medoids algorithm based on density is able to outperform traditional clustering algorithms in terms of both runtime and clustering quality. Overall, the research presented in this paper showcases the potential of combining classic clustering algorithms with modern parallel computing frameworks to address the challenges posed by big data. By leveraging the scalability and efficiency of the Hadoop platform, the proposed algorithm provides a practical solution for extracting valuable insights from large datasets in a timely manner.