Active learning for semi-supervised clustering allows algorithms to solicit a domain expert to provide side information as instances constraints, for example a set of labeled instances called seeds. The problem consists in selecting the queries to the expert that are likely to improve either the relevance or the quality of the proposed clustering. However, these active methods suffer from several limitations: (i) they are generally tailored for only one specific clustering paradigm or cluster shape and size, (ii) they may be counter-productive if the seeds are not selected in an appropriate manner and, (iii) they have to work efficiently with minimal expert supervision. In this paper, we propose a new active seed selection algorithm that relies on a k-nearest neighbors structure to locate dense potential clusters and efficiently query and propagate expert information. Our approach makes no hypothesis on the underlying data distribution and can be paired with any clustering algorithm. Comparative experiments conducted on real data sets show the efficiency of this new approach compared to existing ones.