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Method and system for mining quantitative association rules in large relational tables

  • US 5,724,573 A
  • Filed: 12/22/1995
  • Issued: 03/03/1998
  • Est. Priority Date: 12/22/1995
  • Status: Expired due to Term
First Claim
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1. A method for identifying quantitative association rules from a table of records, each record having a plurality of attributes associated therewith, the attributes including quantitative and categorical attributes, each attribute having a value, the method comprising the steps of:

  • partitioning the values of each quantitative attribute from a selected group of quantitative attributes into a respective plurality of intervals;

    determining a support for each value of the categorical attributes and the non-partitioned quantitative attributes, and a support for each interval of the partitioned quantitative attributes, the support for a value being a number of records in the table whose attribute values include the value, the support for an interval being a number of records in the table whose attribute values are part of the interval;

    for each quantitative attribute, combining adjacent values of the attribute if the attribute is not partitioned, or adjacent intervals of the attribute if the attribute is partitioned, into ranges, as long as the support for each range is less than a maximum support;

    identifying items with at least a minimum support, each item representing a quantitative attribute and a range, or a categorical attribute and a value, the items with at least the minimum support making up a seed set;

    generating candidate itemsets from the seed set, each itemset being a set of items and having a support, the support of the itemset being a number of records in the table which support the itemset;

    determining frequent itemsets from the candidate itemsets, the frequent itemsets being those itemsets whose support is more than the minimum support, the determined frequent itemsets becoming the next seed set;

    repeating the steps of generating candidate itemsets and determining frequent itemsets until all the frequent itemsets are found; and

    outputting an association rule when the support of a selected frequent itemset bears a predetermined relationship to the support of a subset of the selected frequent itemset, thereby satisfying a minimum confidence constraint, the association rule being an expression of the form XY where X and Y are itemsets.

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