Since its inception in 2009, Bitcoin has become and is currently the most successful and widely used cryptocurrency. It introduced blockchain technology, which allows transactions that transfer funds between users to take place online, in an immutable manner. No real-world identities are needed or stored in the blockchain. At the same time, all transactions are publicly available and auditable, making Bitcoin a pseudo-anonymous ledger of transactions. The volume of transactions that are broadcast on a daily basis is considerably large. We propose a set of features that can be extracted from transaction data. Using this, we apply a data processing pipeline to ultimately cluster transactions via a k-means clustering algorithm, according to the transaction properties. Finally, according to these properties, we are able to characterize these clusters and the transactions they include. Our work mainly differentiates from previous studies in that it applies an unsupervised learning method to cluster transactions instead of addresses. Using the novel features we introduce, our work classifies transactions in multiple clusters, while previous studies only attempt binary classification. Results indicate that most transactions fall into a cluster that can be described as common user transactions. Other clusters include transactions made by online exchanges and lending services, those relating to mining activities as well as smaller clusters, one of which contains possibly illicit or fraudulent transactions. We evaluated our results against an online database of addresses that belong to known actors, such as online exchanges, and found that our results generally agree with them, which enhances the validity of our methods.

中文翻译：

---

比特币交易的无监督聚类

自 2009 年诞生以来，比特币已经成为并且是目前最成功、使用最广泛的加密货币。它引入了区块链技术，允许用户之间转移资金的交易以不可变的方式在线进行。区块链中不需要或存储真实世界的身份。同时，所有交易都是公开且可审计的，使比特币成为一个伪匿名的交易分类账。每天广播的交易量相当大。我们提出了一组可以从交易数据中提取的特征。使用此功能，我们根据交易属性应用数据处理管道，通过 k 均值聚类算法最终对交易进行聚类。最后，根据这些属性，我们能够表征这些集群及其包含的交易。我们的工作与之前的研究的主要区别在于，它应用无监督学习方法来集群交易而不是地址。使用我们引入的新功能，我们的工作对多个集群中的交易进行分类，而以前的研究仅尝试二元分类。结果表明，大多数事务属于可以描述为公共用户事务的集群。其他集群包括在线交易和借贷服务进行的交易、与采矿活动相关的交易以及较小的集群，其中之一可能包含非法或欺诈交易。我们根据属于已知参与者的地址在线数据库（例如在线交易所）评估了我们的结果，发现我们的结果通常与它们一致，这增强了我们方法的有效性。