k-means clustering originated from signal processing and is a popular method in data mining. The main intent of k-means clustering is to find some m points of a dataset that can best represent the center of some m-regions in the dataset.

k-means clustering is also known as partition clustering. This means that one needs to specify the number of clusters before any clustering process is started. You can define an objective function that uses the sum of Euclidean distance between a data point and its nearest cluster centroid. One can follow a systematic procedure to minimize this objective function iteratively by finding a brand new set of cluster centers that can lower the value of the objective function iteratively.

k-means clustering is a popular method in cluster analysis. It does not require any assumptions. This means that when a dataset is given and a predetermined number of clusters is labeled as k and when you apply the k-means algorithm, it minimizes the sum-squared...