Download PDFOpen PDF in browserK-means Algorithm of Big Data In MLEasyChair Preprint 138785 pages•Date: July 9, 2024AbstractArtificial intelligence, particularly machine learning, is crucial in emulating human cognitive processes. In the field of machine learning, unsupervised learning plays a vital role in identifying commonalities within data sets and creating clusters. The K-means clustering algorithm serves as a crucial method for this purpose. This study examines the importance of K-means clustering in machine learning applications, specifically in the field of big data analytics. The K-means algorithm is renowned for its simplicity and efficacy in facilitating data aggregation and analysis, allowing data scientists to extract useful insights and make well-informed judgments. Reviews of the literature address applications of K-means clustering in general data analysis, huge data clustering, travel advice, and financial fraud detection. These results highlight the need to improve K-means clustering to achieve higher levels of accuracy, efficiency, and scalability in different data analytical applications. "Review Paper" Keyphrases: Clustering, K-means algorithm, unsupervised learning
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