Incremental Frequent Itemsets Mining with IPPC Tree

Huynh, Van Quoc Phuong; Küng, Josef; Dang, Tran Khanh

doi:10.1007/978-3-319-64468-4_35

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…The experiments are executed on Windows 7 system, the processor is Intel(R) Core(TM) I5-4200H CPU @ 2.80GHz 2.80GHz, 4GB of installed memory (RAM), system type is 64-bit operating system. In the experiments, the performance of FCFPIM is evaluated against TDUP [27], EFUFP [43] and IFIN [46]. The reasons to select the three algorithms for performance evaluation are as follows:…”

Section: Experimental Results and Analysis A Experimental Settingsmentioning

confidence: 99%

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Incremental Frequent Itemsets Mining With FCFP Tree

et al. 2019

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Frequent itemsets mining (FIM) as well as other mining techniques has been being challenged by large scale and rapidly expanding datasets. To address this issue, we propose a solution for incremental frequent itemsets mining using a Full Compression Frequent Pattern Tree (FCFP-Tree) and related algorithms called FCFPIM. Unlike FP-tree, the FCFP-Tree maintains complete information of all the frequent and infrequent items in the original dataset. This allows the FCFPIM algorithm not to waste any scan and computational overhead for the previously processed original dataset when new dataset are added and support changes. Therefore, much processing time is saved. Importantly, FCFPIM adopts an effective tree structure adjustment strategy when the support of some items changes due to the arrival of new data. FCFPIM is conducive to speeding up the performance of incremental FIM. Although the tree structure containing the lossless items information is space-consuming, a compression strategy is used to save space. We conducted experiments to evaluate our solution, and the experimental results show the space-consuming is worthwhile to win the gain of execution efficiency, especially when the support threshold is low.INDEX TERMS Frequent itemsets mining, incremental mining, FP-tree, FCFP-tree, association rule.

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Section: Experimental Results and Analysis A Experimental Settingsmentioning

confidence: 99%

“…In 2017, on the basis of FIN algorithm [44], [45], Deng Z H and Lv S L proposed IFIN algorithm [46], which is based on prefix tree structure (IPPC-tree). Instead of using the global order of items frequency in data sets like FP-tree, IPPC-tree maintains a local order of items in the path from root to leaf node.…”

Section: Tree Based Incremental Miningmentioning

confidence: 99%