Apriori-roaring: frequent pattern mining method based on compressed bitmaps

Colombo, Alexandre; Spolon, Roberta; Manacero, Aleardo; Lobato, Renata Spolon; Cavenaghi, Marcos Antônio

doi:10.1504/ijbidm.2022.123805

Cited by 3 publications

(7 citation statements)

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“…Although the algorithm in Ref.5 has improved the detection effect of massive moving protective objects compared with the algorithm in Ref. 4, the AP value is still low. In Ref.6, when the target detection level is simple or medium, the AP value obtained by the algorithm for massive motion data detection is relatively high, but when the target detection difficulty level is difficult, the AP value is low.…”

Section: Test Resultsmentioning

confidence: 99%

“…The frequent pattern data mining algorithm based on compressed bitmap in Ref. 4, trajectory data mining algorithm based on mobile big data in Refs.5-6 are used as the comparison algorithms of the algorithms in this paper. The detection results of massive moving data of the four algorithms are compared, and the comparison results are shown in Table 3.…”

Section: Test Resultsmentioning

confidence: 99%

“…From the analysis of Table 3, it can be seen that the algorithm based on literature [4] has the lowest average accuracy on the three detection difficulty levels of massive motion data, indicating that this method has the worst detection effect on moving data targets. Although the algorithm in Ref.5 has improved the detection effect of massive moving protective objects compared with the algorithm in Ref.…”

Section: Test Resultsmentioning

confidence: 99%

“…To further demonstrate the consistency of the massive crowd motion data trajectories fitted by the algorithm in this article, the results were quantitatively measured using smoothness. The algorithms in references [4], [5], and [6] were still compared with the algorithm in this article. Four samples were randomly selected from the KITTI test set for testing.…”

Section: Test Resultsmentioning

confidence: 99%

“…Some relevant experts and scholars have conducted research on massive data mining algorithms. For example, ColomboA et al proposed a frequent pattern data mining algorithm based on compressed bitmap [4], which realized data mining by adopting compressed bitmap structure and using less memory to store original data sets and calculate support degree. However, the algorithm adopts compressed bitmap structure, which makes the display information incomplete and can not grasp the geometric information of motion data in time, resulting in poor effect of motion data mining.…”

Section: Introductionmentioning

confidence: 99%

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Design of Reliable Mining Algorithm for Massive Moving Image Data Trajectory

Zeng,

Run,

Djenouri

2024

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To acquire precise, dependable, and credible trajectory information from extensive motion image datasets, this study introduces a robust mining algorithm grounded in trajectory extraction for voluminous motion data. The algorithm leverages an enhanced single-stage object detection model (TFF-SSD) and employs a 3D sparse convolutional neural network for extracting point cloud features from the extensive motion data. Simultaneously, spatial semantic features are derived by integrating spatial and semantic characteristics of the vast motion data. Subsequently, a mutual attention fusion method is applied to amalgamate point cloud features and spatial semantic features into the Faster R-CNN model. This facilitates the swift identification of moving target regions, leading to the extraction of classification and coordinate information from the abundant data. Employing reference points from diverse types of extensive motion data, the algorithm selects associated feature regions for the motion data target, thereby obtaining reference point information indicative of continuous motion. Subsequent fitting of this reference point information enables the reliable mining of trajectories within the extensive motion data. Experimental outcomes demonstrate the algorithm's ability to accurately detect all crowd movements in a running race. The extracted motion data features are diverse, encompassing reference points that facilitate trajectory mining with elevated quality, reliability, and genuine data content.

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Section: Test Resultsmentioning

confidence: 99%

Section: Test Resultsmentioning

confidence: 99%

Section: Test Resultsmentioning

confidence: 99%

Section: Test Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of Reliable Mining Algorithm for Massive Moving Image Data Trajectory

Zeng,

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Djenouri

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A Target Value Determination Method of Energy Supply Equipment Based on Mapreduce Architecture

Wan,

Xing,

Zhou

et al. 2023

2023 Smart City Challenges &Amp;amp; Outcomes for Urban Transformation (SCOUT)

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An Approach of Improved Traversal Merging of Transaction Data for Faster Apriori Algorithm

2023

acss

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In order to improve the operational efficiency of the Apriori algorithm in the data preprocessing stage of large-scale data and achieve overall optimization of the Apriori project, a fast traversal merge pre-processing method is proposed by integrating an adaptive association mining threshold determination method. Firstly, the proposed fast traversal merging method is analyzed and compared with two benchmark algorithms, and the experimental results show that the running time of the fast traversal merging method is much lower than that of the two benchmark methods; secondly, according to the central limit theorem, a data adaptive support threshold setting method is proposed, which can avoid the subjectivity of the minimum support threshold setting in association mining; finally, the two proposed algorithms are applied to Apriori and the results show that the application of the proposed improved method for association mining gives significantly better results than association mining under the better processing of the benchmark algorithm, and thus can significantly improve the efficiency of solving the shopping basket problem.

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Apriori-roaring: frequent pattern mining method based on compressed bitmaps

Cited by 3 publications

References 0 publications

Design of Reliable Mining Algorithm for Massive Moving Image Data Trajectory

Design of Reliable Mining Algorithm for Massive Moving Image Data Trajectory

A Target Value Determination Method of Energy Supply Equipment Based on Mapreduce Architecture

An Approach of Improved Traversal Merging of Transaction Data for Faster Apriori Algorithm

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