Summary: The classical algorithm of \(k\)-means is discussed, one of the most widespread methods in clustering, including both strong points and shortages. Not only is it sensitive to the original clustering center but also it may be affected by outliers. Given these shortages, an improved algorithm is discussed which makes improvements in outliers and the selection of the original clustering center.