一种约束最大频繁项集增量挖掘算法

2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007) Pub Date : 2007-09-18 DOI:10.1109/NPC.2007.110

Han Wang, Lingfu Kong

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引用次数: 4

摘要

在所有关联规则的数据挖掘算法中，增量算法更适合数据集更新。本文提出了一种新的挖掘约束最大频繁项集的算法，即ISL-DM算法。该算法通过约束条件对不能得到或不能成为最大频繁项集的项序列进行过滤，并始终围绕当前最大频繁项集的获取。

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A Constrained Maximum Frequent Itemsets Incremental Mining Algorithm

Among all data mining algorithms of association rules, incremental algorithms fit dataset updating better. This paper proposes a novel algorithm of mining the constrained maximum frequent itemsets namely algorithm ISL-DM. This algorithm filters the item-sequences which can not get or become the maximum frequent itemsets by the constraint conditions, and it can always surround getting the maximum frequent itemsets currently.

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2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007)

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