  Exploring transactions within the Bitcoin blockchain entails examining the
transfer of bitcoins among several hundred million entities. However, it is
often impractical and resource-consuming to study such a vast number of
entities. Consequently, entity clustering serves as an initial step in most
analytical studies. This process often employs heuristics grounded in the
practices and behaviors of these entities. In this research, we delve into the
examination of two widely used heuristics, alongside the introduction of four
novel ones. Our contribution includes the introduction of the
\textit{clustering ratio}, a metric designed to quantify the reduction in the
number of entities achieved by a given heuristic. The assessment of this
reduction ratio plays an important role in justifying the selection of a
specific heuristic for analytical purposes. Given the dynamic nature of the
Bitcoin system, characterized by a continuous increase in the number of
entities on the blockchain, and the evolving behaviors of these entities, we
extend our study to explore the temporal evolution of the clustering ratio for
each heuristic. This temporal analysis enhances our understanding of the
effectiveness of these heuristics over time.
