  The development of clustering heuristics has demonstrated that Bitcoin is not
completely anonymous. Currently, existing clustering heuristics only consider
confirmed transactions recorded in the Bitcoin blockchain. However, unconfirmed
transactions in the mempool have yet to be utilized to improve the performance
of the clustering heuristics.
  In this paper, we bridge this gap by combining unconfirmed and confirmed
transactions for clustering Bitcoin addresses effectively. First, we present a
data collection system for capturing unconfirmed transactions. Two case studies
are performed to show the presence of user behaviors in unconfirmed
transactions not present in confirmed transactions. Next, we apply the
state-of-the-art clustering heuristics to unconfirmed transactions, and the
clustering results can reduce the number of entities after applying, for
example, the co-spend heuristics in confirmed transactions by 2.3%. Finally, we
propose three novel clustering heuristics to capture specific behavior patterns
in unconfirmed transactions, which further reduce the number of entities after
the application of the co-spend heuristics by 9.8%. Our results demonstrate the
utility of unconfirmed transactions in address clustering and further shed
light on the limitations of anonymity in cryptocurrencies. To the best of our
knowledge, this paper is the first to apply the unconfirmed transactions in
Bitcoin to cluster addresses.
