Scaleable data itemsets and association rules

Details Scaleable data itemsets and association rules Scaleable data itemsets and association rules

2005-01-24

2009-02-10

Boccio, Vincent F (Department: 2169)

Data processing: database and file management or data structures

Database design

Data structure types

C707S793000, C707S793000, C707S793000, C707S793000

Reexamination Certificate

active

07490075

ABSTRACT:
The subject invention leverages scaleable itemsets and/or association rules to provide dynamic adjustment of memory usage. This allows the subject invention to provide association rules and/or itemsets with the highest support while utilizing a bounded amount of memory. Thus, a data analysis system and/or method utilizing the subject invention can self-adjust to provide the best association rules and/or itemsets based on available system resources. One instance of the subject invention employs dynamically adjustable minimum support values for data itemsets and/or association rules to facilitate in compensating for memory availability. In yet another instance of the subject invention a prefix tree data structure is utilized to facilitate in constructing itemsets. Memory utilization is then adjusted via pruning and/or reallocation of counter vectors and/or pointer vectors and/or reallocation of nodes of the prefix tree data structure for scaleable data itemsets and/or association rules.

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R. Agrawal, et al., Mining Associations between Sets of Items in Massive Databases, Proc. of the ACM-SIGMOD 1993 Int'l Conference on Management of Data, May 1993, pp. 207-216, Washington D.C.

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