K-means clustering method has been widely used in many areas. However, it is time-consuming when data are in high dimensional space, or when there are many clusters. We try to accelerate its speed by combing our previous work with the simplest version of a fast K-means method that gracefully used the centers' displacement between two iterations. Experimental results show our method not only is several times faster than the fast K-Means method using center displacement; but also accelerates the fast K-means method that used norms-product test only. As a result, the proposed hybrid method is much faster than the plain K-means method. Hence, it is very useful in real-time data mining; examples include medical diagnostics, customer analysis, and vector quantization.
- K-means clustering
- deletion using center displacement
- deletion using norms product
- real-time data mining
- vector quantization