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An Indexed Trie Approach to Incremental Mining of Closed Frequent Itemsets Based on a Galois Lattice Framework

机译:基于Galois格框架的封闭式频繁项集增量挖掘的索引特里方法

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Incrementality is a major challenge in the mining of dynamic databases. In such databases, the maintenance of association rules can be directly mapped into the problem of maintaining closed frequent itemsets. A number of incremental strategies have been proposed earlier with several limitations. A serious limitation is the need to examine the entire family of closed itemsets, whenever there are insertions or deletions in the database. The proposed strategy relies on an efficient and selective update of the closed itemsets using an indexed trie structure. The framework emphasizes on certain fundamental and structural properties of Galois Lattice theory to overcome the limitations of the earlier approaches. Apart from facilitating a selective update, the indexed structure removes the necessity of working with a wholly memory resident trie.
机译:增量性是动态数据库挖掘中的主要挑战。在这样的数据库中,关联规则的维护可以直接映射到维护封闭的频繁项集的问题。较早提出了许多增量策略,但有一些限制。一个严重的限制是,只要数据库中有插入或删除,就需要检查整个封闭项目集系列。所提出的策略依赖于使用索引的特里结构对封闭项目集进行有效且选择性的更新。该框架强调了伽罗瓦格子理论的某些基本和结构特性,以克服早期方法的局限性。除了促进选择性更新之外,索引结构还消除了使用完全内存驻留的trie的必要性。

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