In unsupervised classification, kernel 𝑘-means clustering method has been shown to perform better than conventional 𝑘-means clustering method in identifying non-isotropic clusters in a data set. The space and time requirements of this method are $O(n^2)$, where 𝑛 is the data set size. Because of this quadratic time complexity, the kernel 𝑘-means method is not applicable to work with large data sets. The paper proposes a simple and faster version of the kernel 𝑘-means clustering method, called single pass kernel k-means clustering method. The proposed method works as follows. First, a random sample $\mathcal{S}$ is selected from the data set $\mathcal{D}$. A partition $\Pi_{\mathcal{S}}$ is obtained by applying the conventional kernel 𝑘-means method on the random sample $\mathcal{S}$. The novelty of the paper is, for each cluster in $\Pi_{\mathcal{S}}$, the exact cluster center in the input space is obtained using the gradient descent approach. Finally, each unsampled pattern is assigned to its closest exact cluster center to get a partition of the entire data set. The proposed method needs to scan the data set only once and it is much faster than the conventional kernel 𝑘-means method. The time complexity of this method is $O(s^2+t+nk)$ where 𝑠 is the size of the random sample $\mathcal{S}$, 𝑘 is the number of clusters required, and 𝑡 is the time taken by the gradient descent method (to find exact cluster centers). The space complexity of the method is $O(s^2)$. The proposed method can be easily implemented and is suitable for large data sets, like those in data mining applications. Experimental results show that, with a small loss of quality, the proposed method can significantly reduce the time taken than the conventional kernel 𝑘-means clustering method. The proposed method is also compared with other recent similar methods.