A new technique for simultaneous clustering and dimensionality reduction of functional data is proposed. The observations are projected into a low-dimensional subspace and clustered by means of a func
The dataset has Gaussian Blobs of varying samples, centers and features. The number of samples ranges from 500 to 50,000. Similarly, the number of centers varies from 2 to 100, while the number of fea