Similarity group-by (SGB, for short) has been proposed as a relational database operator to match the needs of emerging database applications. Many SGB operators that extend SQL have been proposed in the literature, e.g., similarity operators in the one-dimensional space. These operators have various semantics. Depending on how these operators are implemented, some of the implementations may lead to different groupings of the data. Hence, if SQL code is ported from one database system to another, it is not guaranteed that the code will produce the same results. In this paper, we investigate the various semantics for the relational similarity group-by operators in the multi-dimensional space. We define the class of order-independent SGB operators that produce the same results regardless of the order in which the input data is presented to them. Using the notion of interval graphs borrowed from graph theory, we prove that, for certain SGB operators, there exist order-independent implementations. For each of these operators, we provide a sample algorithm that is order-independent. Also, we prove that for other SGB operators, there does not exist an order-independent implementation for them, and hence these SGB operators are ill-defined and should not be adopted in extensions to SQL to realize similarity group-by. In this paper, we introduce an SGB operator, namely SGB-All, for grouping multi-dimensional data using similarity. SGB-All forms groups such that a data item, say O, belongs to a group, say G, if and only if O is within a user-defined threshold from all other data items in G. In other words, each group in SGB-All forms a clique of nearby data items in the multi-dimensional space. We prove that SGB-All are order-independent, i.e., there is at least one algorithm for each option that is independent of the presentation order of the input data.