For a large volume of data, the clustering algorithm is of significant importance to categorize and analyze data. Accordingly, choosing the optimal number of clusters (K) is an essential factor, but it also is a tricky problem in big data analysis. More importantly, it is to efficiently determine the best K automatically, which is the main issue in clustering algorithms. Indeed, considering both the quality and efficiency of the clustering algorithm during defining K can be a trade-off that is our primary purpose to overcome. K-Means is still one of the popular clustering algorithms, which has a shortcoming that K needs to be pre-set. We introduce a new process with fewer K-Means running, which selects the most promising time to run the K-Means algorithm. To achieve this goal, we applied Bisecting K-Means and a different splitting measure, which all are contributed to efficiently determine the number of clusters automatically while maintaining the quality of clustering for a large set of high dimensional data. We carried out our experimental studies on different data sets and found that our procedure has the flexibility of choosing different criteria for determining the optimal K under each of them. Experiments indicate higher efficiency through decreasing of computation cost compared with the Ray Turi method or with the use of only the K-Means algorithm.