1. A computer-implemented method for dynamically clustering data items, the method comprising: receiving (a) a plurality of data items originating from at least two sources; (b) a plurality of distinct metadata details; (c) data indicative of associations between said data items and said metadata details, wherein each data item is associated with at least one metadata detail, and wherein at least a first data item originating from a first source and a second data item originating from a second source are related data items associated with at least one shared metadata detail; grading strengths of relationships between at least one of said data items and at least one of said metadata details; and clustering said data items into one or more clusters, based on the calculated grades wherein at least one of said clusters comprises related data items originating from more than one source, wherein said grading comprises applying weighting functions and the weighting functions are rule-based weighting functions.