This paper presents a comprehensive review of existing techniques of k-means clustering algorithms made at various times. The k-means algorithm is aimed at partitioning objects or points to be analyzed into well separated clusters. There are different algorithms for k-means clustering of objects such as traditional k-means algorithm, standard k-means algorithm, basic k-means algorithm and the conventional k-means algorithm, this are perhaps the most widely used versions of the k-means algorithms. These algorithms uses the Euclidean distance as its metric and minimum distance rule approach by assigning each data points (objects) to its closest centroids.