摘要： 比特币是一种基于区块链的加密货币，其因具备伪匿名性而常被用于异常交易活动中。目前比特币实体识别多通过启发式聚类方法实现，但此类方法未考虑新数据出现后的结果融合问题，导致算法效率较低。针对该问题，提出一种基于比特币交易数据的增量聚类方法。对区块数据进行分析以获取钱包地址的可聚类交易，构成聚类地址组，并通过查找地址索引表提取聚类实体间的关系。利用并查集算法对该区块钱包地址数据进行增量聚类，得到新的比特币实体关系，进而推测实体类型。同时，对实体进行识别和标注，实现实体交易行为的可视分析。实验结果表明，该方法可以准确地对地址进行增量聚类，体现比特币实体的演变过程，与启发式聚类方法相比时间复杂度更低。

关键词: 比特币, 区块链交易, 可视分析, 增量聚类, 并查集

Abstract: Bitcoin is a blockchain-based cryptocurrency,which is often used in abnormal transaction activities because of its pseudo-anonymity.Bitcoin entity recognition is often implemented by using the traditional heuristic clustering algorithm,but the algorithm does not consider the problem of result fusion after the emergence of new data.To this end,this paper proposes an incremental clustering method based on the characteristics of Bitcoin transaction data.Firstly,the block data is analysed to obtain the clustering transaction of the wallet address to form a clustering address group.Then,by looking up the address hash table,the relationships between the clustering entities are extracted.Finally,based on the union-find set algorithm,the wallet address data of the block is incrementally clustered to obtain a new Bitcoin entity relationship,and thereby to infer the type of the entity.At the same time,the entities are identified and labelled to implement visual analysis of the transaction behaviour of entities.Experimental results show that the proposed method can accurately implement incremental clustering of addresses,and displays the evolution process of Bitcoin entities.Compared with the heuristic clustering method,the proposed method has lower time complexity.

Key words: Bitcoin, blockchain transaction, visual analysis, incremental clustering, union-find set

中图分类号: 

• TP399