1. A computer-implemented method, comprising: accessing records, wherein: the records include first records and second records; accessing the records is performed on a computing device each of the records includes a respective essential field and a respective nonessential field, and wherein: in each of the first records and in each of the second records, the respective essential field includes an essential substring; in each of the first records, the respective nonessential field includes a nonessential substring; and in each of the second records, the respective nonessential field is empty; for each of the records, deriving: a first matchcode that includes a first fuzzy representation of the respective essential substring; and a second matchcode that includes a second fuzzy representation of the respective essential substring, wherein the second fuzzy representation is different than the first fuzzy representation, and wherein: each of the second matchcodes derived for a first record includes a nonessential field matchcode segment that represents the respective nonessential substring; and each of the second matchcodes derived for a second record includes a wildcard character segment that is deemed to be equivalent to each of the nonessential field matchcode segments; forming first matchcode equivalence clusters by selectively grouping the records such that first matchcodes for records grouped together are equivalent, wherein forming the first matchcode equivalence clusters is done independently of record type; within at least one of the first matchcode equivalence clusters, further grouping at least one of the records with at least one of the other records, wherein grouping within each of the at least one first matchcode equivalence clusters: is done such that second matchcodes for records grouped together are equivalent; and is done independently of similarities between records in different first matchcode equivalence clusters; and identifying matches amongst the records based on the grouping within the at least one first matchcode equivalence cluster.
1. A computer-implemented method, comprising: accessing records, wherein: the records include first records and second records; accessing the records is performed on a computing device each of the records includes a respective essential field and a respective nonessential field, and wherein: in each of the first records and in each of the second records, the respective essential field includes an essential substring; in each of the first records, the respective nonessential field includes a nonessential substring; and in each of the second records, the respective nonessential field is empty; for each of the records, deriving: a first matchcode that includes a first fuzzy representation of the respective essential substring; and a second matchcode that includes a second fuzzy representation of the respective essential substring, wherein the second fuzzy representation is different than the first fuzzy representation, and wherein: each of the second matchcodes derived for a first record includes a nonessential field matchcode segment that represents the respective nonessential substring; and each of the second matchcodes derived for a second record includes a wildcard character segment that is deemed to be equivalent to each of the nonessential field matchcode segments; forming first matchcode equivalence clusters by selectively grouping the records such that first matchcodes for records grouped together are equivalent, wherein forming the first matchcode equivalence clusters is done independently of record type; within at least one of the first matchcode equivalence clusters, further grouping at least one of the records with at least one of the other records, wherein grouping within each of the at least one first matchcode equivalence clusters: is done such that second matchcodes for records grouped together are equivalent; and is done independently of similarities between records in different first matchcode equivalence clusters; and identifying matches amongst the records based on the grouping within the at least one first matchcode equivalence cluster. 7. The method of claim 1 , wherein: each of the first fuzzy representations is a phonetic reduction of the respective essential substring.