FPO tree and DP3 algorithm for distributed parallel Frequent Itemsets Mining

Huynh, Van Quoc Phuong; Küng, Josef

doi:10.1016/j.eswa.2019.112874

Cited by 13 publications

(2 citation statements)

References 12 publications

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“…Thus, the tree serves as an index structure that allows to efficiently find the covering rules of an example whose corresponding selectors are also in the order O. The structure of an R-tree is quite similar to that of an FPO-tree (Huynh & Küng, 2020), which provides optimal compactness and efficient aggregations for very large numbers of local frequent itemsets. A minor difference is that the R-tree indexes references to rules whereas the FPOtree records support counts of itemsets.…”

Section: R-treementioning

confidence: 99%

Efficient learning of large sets of locally optimal classification rules

2023

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Conventional rule learning algorithms aim at finding a set of simple rules, where each rule covers as many examples as possible. In this paper, we argue that the rules found in this way may not be the optimal explanations for each of the examples they cover. Instead, we propose an efficient algorithm that aims at finding the best rule covering each training example in a greedy optimization consisting of one specialization and one generalization loop. These locally optimal rules are collected and then filtered for a final rule set, which is much larger than the sets learned by conventional rule learning algorithms. A new example is classified by selecting the best among the rules that cover this example. In our experiments on small to very large datasets, the approach’s average classification accuracy is higher than that of state-of-the-art rule learning algorithms. Moreover, the algorithm is highly efficient and can inherently be processed in parallel without affecting the learned rule set and so the classification accuracy. We thus believe that it closes an important gap for large-scale classification rule induction.

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Section: R-treementioning

confidence: 99%

Efficient learning of large sets of locally optimal classification rules

2023

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“…Analyzing the above research, it is found that since the research history of the maximum frequent itemset mining algorithm is not long, there are still many deficiencies in the efficiency of the algorithms [ 25 , 26 ]. However, the challenge of mining the maximum frequent items lies in the huge amount of data, and the efficiency of the algorithm is the key.…”

Section: Introductionmentioning

confidence: 99%

The Mining Algorithm of Maximum Frequent Itemsets Based on Frequent Pattern Tree

Xi-feng

2022

Computational Intelligence and Neuroscience

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In the discipline of data mining, association rule mining is an important study topic that focuses on discovering the relationships between database attributes. The maximum frequent itemset comprises the information of all frequent itemsets, which is one of the important difficulties in mining association rules, and certain data mining applications just need to mine the maximum frequent itemsets. As a result, analyzing the maximum frequent itemset mining technique is practical. Considering this, the research introduces FP-MFIA, a new maximum frequent itemset mining approach based on the FP-tree, which is inspired by the data structure of the frequent pattern tree and the idea that the maximum frequent itemset implies all frequent itemsets. First, the FP-MFIA constructs a one-way FP-tree structure, which only has pointers from the root to the leaves, so that only two scans of the FP-tree are required by the FP-MFIA. On the other hand, it redefines a data storage structure MFI-list for maximum frequent itemsets. It can quickly release unnecessary nodes in the FP-tree after scanning it. In this way, not only the information required by the maximum frequent itemsets can be quickly mined but also the space required for storing the maximum frequent itemsets can be reduced, which greatly improves the mining efficiency. Finally, experiments were conducted to compare the mining efficiency of the novel FP-MFIA algorithm to the IDMFIA and DMFIA algorithms. We can see from the findings that the FP-MFIA algorithm is more efficient than the other two techniques.

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A Parallelized Frequent Temporal Pattern Mining Algorithm on a Time Series Database

2021

Intelligent Information and Database Systems

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FPO tree and DP3 algorithm for distributed parallel Frequent Itemsets Mining

Cited by 13 publications

References 12 publications

Efficient learning of large sets of locally optimal classification rules

Efficient learning of large sets of locally optimal classification rules

The Mining Algorithm of Maximum Frequent Itemsets Based on Frequent Pattern Tree

A Parallelized Frequent Temporal Pattern Mining Algorithm on a Time Series Database

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