Enhancing Itemset Tree Rules and Performance

Lewis, Jay B.; Benton, Ryan; Bourrie, David; Lavergne, Jennifer

doi:10.1109/bigdata47090.2019.9006305

Cited by 3 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MEIT is a better data structure that reduces unnecessary nodes of Itemset-Tree, thus improving the efficiency of targeted queries in itemset mining and association rule mining. In order to solve the problem that the Itemset-Tree cannot utilize Apriori property and many invalid operations in the mining process, Lewis et al [30] designed a fast generation process to improve efficiency. In addition, query-constraint-based ARM (QARM) [1] was introduced to analyze a wide variety of clinical datasets in the National Sleep Research Resource.…”

Section: Targeted Pattern Queryingmentioning

confidence: 99%

TaSPM: Targeted Sequential Pattern Mining

Huang¹,

Gan²,

Yu³

2022

Preprint

View full text Add to dashboard Cite

Sequential pattern mining (SPM) is an important technique of pattern mining, which has many applications in reality. Although many efficient sequential pattern mining algorithms have been proposed, there are few studies can focus on target sequences. Targeted querying sequential patterns can not only reduce the number of sequences generated by SPM, but also improve the efficiency of users in performing pattern analysis. The current algorithms available on targeted sequence querying are based on specific scenarios and cannot be generalized to other applications. In this paper, we formulate the problem of targeted sequential pattern mining and propose a generic framework namely TaSPM, based on the fast CM-SPAM algorithm. What's more, to improve the efficiency of TaSPM on large-scale datasets and multiple-items-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in mining processes. Totally four pruning strategies are designed in TaSPM, and hence it can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conduct extensive experiments on different datasets to compare the existing SPM algorithms with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.

show abstract

Section: Targeted Pattern Queryingmentioning

confidence: 99%

TaSPM: Targeted Sequential Pattern Mining

Huang¹,

Gan²,

Yu³

2022

Preprint

View full text Add to dashboard Cite

show abstract

Mining Rare Utility Patterns within Target Items

Xu,

Peng,

Chen

et al. 2023

2023 IEEE International Conference on Big Data (BigData)

View full text Add to dashboard Cite

TaSPM: Targeted Sequential Pattern Mining

Huang,

Gan,

2024

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

Sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted querying of the concerned sequential patterns can not only reduce the number of patterns generated, but also increase the efficiency of users in performing related analysis. The current algorithms available for targeted sequence querying are based on specific scenarios and can not be extended to other applications. In this paper, we formulate the problem of targeted sequential pattern mining and propose a generic algorithm, namely TaSPM. What is more, to improve the efficiency of TaSPM on large-scale datasets and multiple-item-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in the mining process. Totally four pruning strategies are designed in TaSPM, and hence TaSPM can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conducted extensive experiments on different datasets to compare the baseline SPM algorithm with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.

show abstract

Enhancing Itemset Tree Rules and Performance

Cited by 3 publications

References 21 publications

TaSPM: Targeted Sequential Pattern Mining

TaSPM: Targeted Sequential Pattern Mining

Mining Rare Utility Patterns within Target Items

TaSPM: Targeted Sequential Pattern Mining

Contact Info

Product

Resources

About