Finding Groups in Large Data Sets

This paper aims to give an overview of methods to find groups in large data sets, such as household expenditure survey data. These methods are grouped in three: cluster analysis, dimension reduction and basic explorative methods. The emphasis is put on a critical analysis and potential drawbacks, especially of inputs that have to be provided by the researcher. These may impose some structure not present in the data, thus defeating the purpose of revealing intrinsic patterns. In general, the more elaborate methods, such as cluster analysis, are delicate to apply, especially in the context of social sciences. Often, it may be best to limit oneself to more transparent approaches such as comparisons of basic statistics.