A Study of Clustering Algorithms on Big Data using Spark

Big data clustering on Spark is a practical method that makes use of Apache Spark’s distributed computing capabilities to handle clustering tasks on massive datasets such as big data sets. Using the unsupervised learning technique of clustering, related data points are grouped together to find underlying structures and patterns. Spark’s strong platform overcomes the performance and scalability restrictions of conventional clustering techniques, allowing effective and scalable clustering operations. The performance and dependability of big data clustering on Spark are further improved by fault tolerance and in-memory processing. By employing popular clustering algorithms like k-means, hierarchical, bisecting, GMM, and BIRCH, researchers and practitioners can extract valuable insights from extensive datasets, making informed decisions and unlocking meaningful patterns on a large scale.