Time series clustering finds applications in diverse fields of science and technology. Kernel based clustering methods like kernel K-means method need number of clusters as input and cannot handle outliers or noise. In this paper, we propose a density based clustering method in kernel feature space for clustering multivariate time series data of varying length. This method can also be used for clustering any type of structured data, provided a kernel which can handle that kind of data is used. We present heuristic methods to find the initial values of the parameters used in our proposed algorithm. To show the effectiveness of this method, this method is applied to two different online handwritten character data sets which are multivariate time series data of varying length, as a real world application. The performance of the proposed method is compared with the spectral clustering and kernel k-means clustering methods. Besides handling outliers, the proposed method performs as well as the spectral clustering method and outperforms the kernel k-means clustering method.