International Business Machines Corporation

Manohar Bashyam

Pedro Miguel Barbas

Enzo Cialini

David Kelly

Clara Liu

Embodiments of the present invention provide systems and methods for associating rule mining in data comprising first and second variables, by generating a first and second filtering bitmap. The first filtering bitmap represents a set of values for the second variable for each of a plurality of values of the first variable. The second filtering bitmap represents, associations between frequent values of the second variable, which enables the identification of frequent 2-pattern itemsets. Thus, by performing first logical operations on the frequent 2-pattern itemsets, frequent high order-pattern itemsets may be identified. A max pattern itemset may be identified among the frequent high order-pattern itemsets. As a result, embodiments may mitigate performance and stability problems associated with conventional association rule mining approaches.

Association rule mining using max pattern transactions

Embodiments of the present invention provide systems and methods for associating rule mining. By way of example, embodiments obviate or mitigate performance and stability problems associated with conventional association rule mining approaches, by proposing concepts implementing association rule mining using rolling filtering bitmaps over dynamic data.

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Austin, TX, United States of America

Manohar Bashyam