Temporal Data Mining is directed at the identification of knowledge that has some temporal dimension. This paper reports on work conducted to identify temporal frequent patterns in social network data. The focus for the work is the cattle movement database in operation in Great Britain, which can be interpreted as a social network with additional spatial and temporal information. The paper firstly proposes a trend mining framework for identifying frequent pattern trends. Experiments using this framework demonstrate that in many cases a large number of patterns may be produced, and consequently the analysis of the end result is inhibited. To assist in the analysis of the identified trends this paper secondly proposes a trend clustering approach, founded on the concept of Self Organizing Maps (SOMs), to group similar trends and to compare such groups. A distance function is used to compare and analyze the changes in clusters with respect to time.