A new clustering method called K-means with Improved Initial Center was developed to avoid randomness in selecting initial centers. The algorithm improves the initial value selection by using the square root of the number of data points for the initial clustering number and combining categories through a sub-merger strategy. This method does not require users to specify the number of clusters beforehand. Experiments on synthetic datasets showed significant improvements in clustering accuracy compared to traditional random K-means.