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首页> 外文期刊>International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems >An Efficient Approach for Incremental Mining Fuzzy Frequent Itemsets with FP-Tree
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An Efficient Approach for Incremental Mining Fuzzy Frequent Itemsets with FP-Tree

机译：FP-Tree增量挖掘模糊频繁项集的一种有效方法

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摘要

Keeping the generated fuzzy frequent itemsets up-to-date and discovering the new fuzzy frequent itemsets are challenging problems in dynamic databases. In this paper, the classical H-struct structure is extended to mining fuzzy frequent itemsets. The extended H-mine algorithm can use any t-norm operator to calculate the support of fuzzy itemset. The FP-tree-based structure called the Initial-FP-tree and the New-FP-tree are built to maintain the fuzzy frequent itemsets in the original database and the new inserted transactions respectively. The strategy of incremental mining of fuzzy frequent itemsets is achieved by breath-first-traversing the Initial-FP-tree and the New-FP-tree. All of the fuzzy frequent itemsets in the updated database can be obtained by traversing the Initial FP -tree. The experiments on real datasets show that the proposed approach runs faster than the batch extended H-mine algorithm. Comparing with the existing algorithm for incremental mining fuzzy frequent itemsets, the proposed approach is superior in terms of the execution time. The memory cost of the proposed approach is lower than that of the existing algorithm when the minimum support threshold is low.
机译：在动态数据库中，使生成的模糊频繁项集保持最新并发现新的模糊频繁项集是具有挑战性的问题。本文将经典的H结构扩展为挖掘模糊频繁项集。扩展的H-mine算法可以使用任何t范数运算符来计算模糊项集的支持度。建立基于FP树的结构，称为Initial-FP-tree和New-FP-tree，以分别在原始数据库和新插入的事务中维护模糊频繁项集。通过频繁呼吸遍历Initial-FP-tree和New-FP-tree来实现模糊频繁项集的增量挖掘策略。可以通过遍历初始FP树来获取更新的数据库中的所有模糊频繁项集。在真实数据集上的实验表明，该方法比批处理扩展的H-mine算法运行得更快。与现有的增量挖掘模糊频繁项集算法相比，该方法在执行时间上具有优势。当最小支持阈值低时，所提出的方法的存储器成本低于现有算法的存储器成本。

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