K-means Algorithm of Big Data In ML

EasyChair Preprint no. 13878

Abstract

Artificial 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

@Booklet{EasyChair:13878,
  author = {Ghadeer Suleiman},
  title = {K-means Algorithm of Big Data In ML},
  howpublished = {EasyChair Preprint no. 13878},

  year = {EasyChair, 2024}}