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This paper addresses the baffling problem of name disam- biguation in the context of digital libraries that administer bibliographic citations. The problem emanates when multi- ple authors share a common name or when multiple name variations of an author appear in citation records. Name dis- ambiguation is not trivial to solve, and most of the digital libraries do not provide an efficient way to accurately iden- tify the citation records of an author. Furthermore, lack of complete meta-data information in digital libraries hinders the existence of generic algorithm that can be applicable on any dataset. We propose a heuristic-based, unsupervised and adaptive method that also embraces users’ interaction to count users’ feedback in disambiguation process. Moreover, the method exploits important features associated with an author and citation records such as co-authors, affiliation, publication title, venue etc., and contrives a conspicuous multilayer hierarchical clustering algorithm, which tunes it- self according to the available information and form clusters of unambiguous records. Our experiments on a set of re- searchers that are contemplated to be highly ambiguous de- cisively produced high precision and recall results and affirm the viability of our algorithm.