An address in the Bitcoin blockchain serves as an identifier for spending cryptocurrency. The blockchain itself does not contain information about the actual users who control the assets. Users have the ability to create multiple addresses, leading to the challenge of grouping addresses, also known as clustering, in order to analyze Bitcoin users. The grouping problem is a crucial initial step in the analysis of Bitcoin users. The grouping solution involves using heuristics based on usage patterns, with a focus on Common Spending (CS) and One-Time Change (OTC) in the current research. Additionally, anonymization techniques such as Shared Send Mixers (SSM) are considered in this paper as they prevent or at least complicate analysis. It is possible to untangle SSM, and based on the number of untanglings per transaction and their size, the transaction can be classified into a certain complexity class. Both heuristics and untangling were applied to the Bitcoin transaction history in our study. Our findings revealed that OTC misuse may occur in 19 to 26 percent of cases, depending on the specific algorithm used. Furthermore, CS and OTC were found to generate 13 to 0.8 percent of new cases when applied to subtransactions of separable SSM. Additionally, we demonstrated that SSM address grouping respects untangling complexity classes. In the future, we plan to adapt our workflow to other Bitcoin-like blockchains and modify our untangling algorithm to cover more SSM transactions.