In modern scientific research, data analyses are often used as a popular tool across computer science, communication science, and biological science. Clustering plays a significant role in the reference composition of data analysis. Clustering, recognized as an essential issueofun supervised learning, deals with the segmentation of the data structure in an unknown region and is the basis for further understanding. Among many clustering algorithms, “more than 100 clusteringalgorithmsknown”becauseofitssimplicityandrapidconvergence,the K-means clustering algorithm is commonly used. This paper explains the different applications, literature,challenges, methodologies, considerations of clustering methods,and related key objectives to implement clustering with big data. Also, presents one of the most common clustering techniques for identification of data patterns by performing an analysis of sample data.

Clustering;dataanalysis;datamining;unsupervised learning;k-mean;algorithms.