1. A computer implemented method of grouping spend items in a list of said spend items and detecting outliers, comprising the steps of: generating word tokens and sound codes for the spend items in said spend item list; phonetically sorting the spend items using said sound codes; grouping said phonetically sorted spend items into second level clusters, comprising the steps of: assigning first level identifiers to the sorted spend items; sorting the sound codes of each sorted spend item; creating first level clusters by matching sorted spend items using said word tokens and said sorted sound codes, whereby said first level identifiers of said matched spend items are updated with a common second level identifier; generating a list of unique spend items, wherein each of said unique spend items represents each of said first level clusters; creating said second level clusters by matching the unique spend items using the word tokens and sound codes, whereby said second level identifiers of matched unique spend items are updated with a common third level identifier; updating the first level clusters represented by the unique spend item with said third level identifier; and detecting statistically deviating outliers in each of the second level clusters, wherein said step of detecting outliers comprises determining central tendency of prices of spend items within each of the second level clusters, wherein said central tendency is one of a mean, a median and a mode; whereby discrepancies in clustering and spend data are determined by grouping the spend items into the second level clusters and detecting the outliers in each of the second level clusters.
1. A computer implemented method of grouping spend items in a list of said spend items and detecting outliers, comprising the steps of: generating word tokens and sound codes for the spend items in said spend item list; phonetically sorting the spend items using said sound codes; grouping said phonetically sorted spend items into second level clusters, comprising the steps of: assigning first level identifiers to the sorted spend items; sorting the sound codes of each sorted spend item; creating first level clusters by matching sorted spend items using said word tokens and said sorted sound codes, whereby said first level identifiers of said matched spend items are updated with a common second level identifier; generating a list of unique spend items, wherein each of said unique spend items represents each of said first level clusters; creating said second level clusters by matching the unique spend items using the word tokens and sound codes, whereby said second level identifiers of matched unique spend items are updated with a common third level identifier; updating the first level clusters represented by the unique spend item with said third level identifier; and detecting statistically deviating outliers in each of the second level clusters, wherein said step of detecting outliers comprises determining central tendency of prices of spend items within each of the second level clusters, wherein said central tendency is one of a mean, a median and a mode; whereby discrepancies in clustering and spend data are determined by grouping the spend items into the second level clusters and detecting the outliers in each of the second level clusters. 8. The computer implemented method of claim 1 , wherein said step of detecting outliers further comprises setting thresholds for frequent and non-frequent suppliers.