Using principles of object oriented design, an iterative k-means clustering algorithm was written to cluster data into similar groups, based on their mean. In the case of this algorithm, the euclidian distance between points in space is used. The user chooses a 'k', which is the number of clusters, or groups, to separate the data into. Using this parameter, the program reads a file of points and iteratively groups them into the 'k' different clusters, until a threshold is reached. GitHub Repository for K-Means Algorithm