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.

翻译：聚类启发方法的发展表明，比特币并非完全匿名。目前，现有的聚类启发方法仅考虑记录在比特币区块链上的已确认交易。然而，内存池中的未确认交易尚未被用于提升聚类启发方法的性能。在本文中，我们通过结合未确认交易与已确认交易来有效聚类比特币地址，从而弥补了这一空白。首先，我们提出一个用于捕获未确认交易的数据收集系统。通过两个案例研究，展示了未确认交易中存在的用户行为模式，而这类行为在已确认交易中并不出现。接下来，我们将最先进的聚类启发方法应用于未确认交易，聚类结果可使后续应用（例如，已确认交易中的共同花费启发方法）后的实体数量减少2.3%。最后，我们提出三种新型聚类启发方法，用于捕获未确认交易中的特定行为模式，这些方法进一步使应用共同花费启发方法后的实体数量减少9.8%。我们的结果证明了未确认交易在地址聚类中的效用，并进一步揭示了加密货币匿名性的局限性。据我们所知，本文是首个将比特币中的未确认交易应用于地址聚类的研究。