1. A method comprising, with at least one computing device: obtaining one or more electrical signals representing a plurality of clusters, each of said plurality of clusters comprising a plurality of documents; for each cluster of a subset of said plurality of clusters: determining one or more static content features of said plurality of documents in said cluster as being local to said cluster rather than common to said subset of said plurality of clusters, identifying one or more structural features of said plurality of documents in said cluster, and determining a weight value for at least one of said one or more structural features based, at least in part, on a measure of uncertainty corresponding to an informativeness value of content for said at least one of said one or more structural features across said plurality of documents in said cluster, said measure of uncertainty based, at least in part, on a computed divergence, with higher divergence corresponding to more informative regions of said plurality of documents in said cluster; for said subset of said plurality of clusters: determining a subset cluster similarity value based, at least in part, on: said one or more structural features of said plurality of documents of said each cluster in said subset of said clusters, said weight value, and said one or more static content features of said plurality of documents of said each cluster in said subset of said clusters; and merging at least one said subset of said clusters to form a super cluster based, at least in part, on said subset cluster similarity value and at least one desired consistency threshold value.