A New Algorithm for Extracting Textual Maximal Frequent Itemsets from Arabic Documents

Hamid, Zeyad; Khafaji, Hussein K.

doi:10.1088/1742-6596/1773/1/012012

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…proposed Right Extension (RHSE) algorithm, which can accurately locate all MFIs. Khafaji et al (2021) proposed an algorithm for extracting the maximum frequent itemset from Arabic text datasets. The algorithm starts at the selected initial boundary in the search space and then moves up and down to generate the required maximum frequent itemset.…”

Section: Maximum Frequent Itemset Mining Methodsmentioning

confidence: 99%

Discovery of User Groups Densely Connecting Virtual and Physical Worlds in Event-Based Social Networks

Lan

2023

International Journal of Information Technologies and Systems Approach

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An essential task of the event-based social network (EBSN) platform is to recommend events to user groups. Usually, users are more willing to participate in events and interest groups with their friends, forming a particularly closely connected user group. However, such groups do not explicitly exist in EBSN. Therefore, studying how to discover groups composed of users who frequently participate in events and interest groups in EBSN has essential theoretical and practical significance. This article proposes the problem of discovering maximum k fully connected user groups. To address this issue, this article designs and implements three algorithms: a search algorithm based on Max-miner (MMBS), a search algorithm based on two vectors (TVBS) and enumeration tree, and a divide-and-conquer parallel search algorithm (DCPS). The authors conducted experiments on real datasets. The comparison of experimental results of these three algorithms on datasets from different cities shows that the DCPS algorithm and TVBS algorithm significantly accelerate their computational time when the minimum support rate is low. The time consumption of DCPS algorithm can reach one tenth or even lower than that of MMBS algorithm.

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Section: Maximum Frequent Itemset Mining Methodsmentioning

confidence: 99%

Discovery of User Groups Densely Connecting Virtual and Physical Worlds in Event-Based Social Networks

Lan

2023

International Journal of Information Technologies and Systems Approach

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“…Therefore, maximal frequent itemsets (MFI) approach is undertaken to overcome the forementioned problems by significantly reducing the search space. Various algorithms were proposed for mining MFI, such as MAFIA [11], FPmax algorithm [12], MaxMining [13], GenMax [14], and MIMA [15,16] which dedicated to mine textual MFI from Arabic documents.…”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Arabic Document Clustering Utilizing Maximal Wordsets

Salman,

Khafaji

2024

RIA

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“…For example, the more classic maximum frequent itemset mining algorithms are Max-Miner [ 16 ], DepthProject [ 17 ], GenMax [ 18 ], MAFIA [ 19 ], FP-max [ 20 ], and Pincer-Search [ 21 ]. What's more is that the well-known maximum frequent itemset mining algorithms also include the algorithm DMFI proposed in literature [ 22 ], the algorithm DMFIA proposed in literature [ 23 ], and the algorithm IDMFIA used in literature [ 24 ]. The Max-Miner was first proposed by Bayardo et al in 1998.…”

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 New Algorithm for Extracting Textual Maximal Frequent Itemsets from Arabic Documents

Cited by 4 publications

References 11 publications

Discovery of User Groups Densely Connecting Virtual and Physical Worlds in Event-Based Social Networks

Discovery of User Groups Densely Connecting Virtual and Physical Worlds in Event-Based Social Networks

A New Algorithm for Arabic Document Clustering Utilizing Maximal Wordsets

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

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