I must find frequent patterns in temporal data, using a method that was imposed to me. This tool has problems handling these data: processing is long and takes a lot of memory. So, I would like to reduce the number of attributes. Also, I want to keep working with the original attributes (no transformation or combination). In other words, I would like to perform a feature selection. By the way, the data contains both numerical and nominal (or categorical) attributes.
The first thing that comes to mind is to identify groups of correlated (or, more generally: associated) attributes and only keep one to represent each group. But I suppose more advanced, and possibly more efficient, methods exist. However, the only articles I can find on the Web are related to using pattern mining to perform feature selection, which is not what I want to do. Moreover, when I look up the feature selection methods described in the literature, they are designed for classification problems.
So, I was wondering if there are any works focusing on feature selection, specifically for pattern mining.