Frequent itemset counting using subsets of bitmaps
A method and mechanism for performing improved frequent itemset operations is provided. A set of item groups are divided into a plurality of subsets. Each item group is composed of a set of data items. Possible combinations of data items that may frequently appear together in the same item group are...
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creator | MOZES ARI W LI WEI JAKOBSSON HAKAN |
description | A method and mechanism for performing improved frequent itemset operations is provided. A set of item groups are divided into a plurality of subsets. Each item group is composed of a set of data items. Possible combinations of data items that may frequently appear together in the same item group are referred to as candidate combinations. Candidate combinations comprising a first set of data items are identified, and thereafter the occurrence of each candidate combination in any item group in each subset is counted by comparing item bitmaps, associated with items in the candidate combination, in each subset in turn. The comparison of item bitmaps is performed in volatile memory. A total frequent itemset count that describes the frequency of candidate combinations in items groups across all subsets is obtained. Thereafter, the total frequent itemset count for candidate combinations having a larger number of data items may be determined. |
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Each item group is composed of a set of data items. Possible combinations of data items that may frequently appear together in the same item group are referred to as candidate combinations. Candidate combinations comprising a first set of data items are identified, and thereafter the occurrence of each candidate combination in any item group in each subset is counted by comparing item bitmaps, associated with items in the candidate combination, in each subset in turn. The comparison of item bitmaps is performed in volatile memory. A total frequent itemset count that describes the frequency of candidate combinations in items groups across all subsets is obtained. 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A set of item groups are divided into a plurality of subsets. Each item group is composed of a set of data items. Possible combinations of data items that may frequently appear together in the same item group are referred to as candidate combinations. Candidate combinations comprising a first set of data items are identified, and thereafter the occurrence of each candidate combination in any item group in each subset is counted by comparing item bitmaps, associated with items in the candidate combination, in each subset in turn. The comparison of item bitmaps is performed in volatile memory. A total frequent itemset count that describes the frequency of candidate combinations in items groups across all subsets is obtained. 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A set of item groups are divided into a plurality of subsets. Each item group is composed of a set of data items. Possible combinations of data items that may frequently appear together in the same item group are referred to as candidate combinations. Candidate combinations comprising a first set of data items are identified, and thereafter the occurrence of each candidate combination in any item group in each subset is counted by comparing item bitmaps, associated with items in the candidate combination, in each subset in turn. The comparison of item bitmaps is performed in volatile memory. A total frequent itemset count that describes the frequency of candidate combinations in items groups across all subsets is obtained. Thereafter, the total frequent itemset count for candidate combinations having a larger number of data items may be determined.</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
title | Frequent itemset counting using subsets of bitmaps |
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