Association Rules Mining Algorithm Based on Linked List
Lin Zhang, Jian Li Zhang
Abstract
Lin Zhang, Jian Li Zhang
Abstract
The paper gave a new association rules mining algorithm based on linked list at the traditional association rules mining algorithm which based on frequent item sets and always ignore the exception rules. The algorithm first scanned the database once and generated the association rules including frequent association rules and exception rules by uses the logical and set theory operations. After example analysis, the algorithm not only with high accuracy and low cost, but also can provide some reference data for exception knowledge mining by generate exception rules.
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The paper gave a new association rules mining algorithm based on linked list at the traditional association rules mining algorithm which based on frequent item sets and always ignore the exception rules. The algorithm first scanned the database once and generated the association rules including frequent association rules and exception rules by uses the logical and set theory operations. After example analysis, the algorithm not only with high accuracy and low cost, but also can provide some reference data for exception knowledge mining by generate exception rules.
Key concepts: Association rule learning, Data mining, Computer science, Apriori algorithm, Association (psychology), Set (abstract data type), Algorithm, Epistemology