Nearest-neighbour clusters as a novel technique for assessing group associations.

ABSTRACT: When all the individuals in a social group can be easily identified, one of the simplest measures of social interaction that can be recorded is nearest-neighbour identity. Many field studies use sequential scan samples of groups to build up association metrics using these nearest-neighbour identities. Here, I describe a simple technique for identifying clusters of associated individuals within groups that uses nearest-neighbour identity data. Using computer-generated datasets with known associations, I demonstrate that this clustering technique can be used to build data suitable for association metrics, and that it can generate comparable metrics to raw nearest-neighbour data, but with much less initial data. This technique could therefore be of use where it is difficult to generate large

SUBMITTER: Rands SA 

PROVIDER: S-EPMC4448799 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature