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An Algorithm and Its Updating Algorithm Based on FP-Tree for Mining Maximum Frequent Itemsets

Yuqing Song, Yuquan Zhu, Sun Zhi-hui, Geng Chen, Chen An

Open publisher page 12 citations

Abstract

Mining maximum frequent itemsets is a key problem in many data mining application. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. In this paper, a fast algorithm DMFIA (discover maximum frequent itemsets algorithm) and its updating algorithm UMFIA (update maximum frequent itemsets algorithm) based on frequent pattern tree (FP-tree) for mining maximum frequent itemsets is proposed. The algorithm UMFIA makes use of previous mining result to cut down the cost of finding

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What this paper is about

Mining maximum frequent itemsets is a key problem in many data mining application. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. In this paper, a fast algorithm DMFIA (discover maximum frequent itemsets algorithm) and its updating algorithm UMFIA (update maximum frequent itemsets algorithm) based on frequent pattern tree (FP-tree) for mining maximum frequent itemsets is proposed. The algorithm UMFIA makes use of previous mining result to cut down the cost of finding

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OpenAlex reports 12 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Mining maximum frequent itemsets is a key problem in many data mining application. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. In this paper, a fast algorithm DMFIA (discover maximum frequent itemsets algorithm) and its updating algorithm UMFIA (update maximum frequent itemsets algorithm) based on frequent pattern tree (FP-tree) for mining maximum frequent itemsets is proposed. The algorithm UMFIA makes use of previous mining result to cut down the cost of finding

Key concepts: Data mining, Computer science, Set (abstract data type), Tree (set theory), Apriori algorithm, Key (lock), Association rule learning, GSP Algorithm

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