The paper introduces the problem of mining association rules in large relational tables that contain both quantitative and categorical attributes. The authors address the challenge of handling quantitative attributes by partitioning their values into intervals and combining adjacent partitions as necessary. They introduce measures of partial completeness to quantify the information loss due to partitioning and use a "greater-than-expected-value" interest measure to identify interesting rules in the output. The paper presents an algorithm for mining such quantitative association rules and evaluates its effectiveness on a real-life dataset. The results show that the number of interesting rules decreases as the partial completeness level increases, and the percentage of rules pruned also decreases, indicating better pruning of similar rules. The interest measure helps identify more interesting rules, and the algorithm scales well with the size of the dataset.The paper introduces the problem of mining association rules in large relational tables that contain both quantitative and categorical attributes. The authors address the challenge of handling quantitative attributes by partitioning their values into intervals and combining adjacent partitions as necessary. They introduce measures of partial completeness to quantify the information loss due to partitioning and use a "greater-than-expected-value" interest measure to identify interesting rules in the output. The paper presents an algorithm for mining such quantitative association rules and evaluates its effectiveness on a real-life dataset. The results show that the number of interesting rules decreases as the partial completeness level increases, and the percentage of rules pruned also decreases, indicating better pruning of similar rules. The interest measure helps identify more interesting rules, and the algorithm scales well with the size of the dataset.