In the digital age, professionals must be able to understand massive, complex data sets. We need speedy assessment and discovery strategies to make sense of this deluge of information. Cluster analysis is a popular data mining method since it allows for simultaneous data processing. Clustering similar data points increases similarity within each group and dissimilarity between them. Clustering works in information retrieval, machine learning, image analysis, and pattern analysis. Clustering methods are not easily parallelizable and perform poorly with huge datasets, making it difficult to apply them to large datasets. How can we cluster this large dataset and finish quickly? This document summarizes the top ten clustering methods in the past decade. It also shows large data clustering method trends. Novel clustering algorithms are also tested on big data sets. Advanced clustering techniques are often discussed in the information age.

Clustering, Big data, methods, K-means method, data mining.