Classification of multidimensional data to multidimensional clusters with a varying subcluster structure
You can pose this as multi-class classification problem (all sub-clusters becomes classes). Since, your input length varies, you should pad your input to get the length equal for all inputs. You can then use neural networks (1D Conv layers followed by Dense and Softmax) to classify this.
An alternate approach to do this would be using tree-based approach where missing values can be handled. Look at CatBoost classifier. This would also require you to pose your problem as multi-class classification.