We consider the problem of machine unlearning to erase a target data, which is used in training but incorrect or sensitive, from a trained model while the training dataset is inaccessible. Previous works have assumed that the target data completely represent all the data to be erased. However, it is often infeasible to indicate all the data to be erased. We hence address a practical scenario of unlearning from a few samples of target data, so-called few-shot unlearning. To this end, we devise a few-shot unlearning method. We demonstrate that our method using only a subset of target data can outperform the state-of-the-art methods with a full indication of target data.