Pamalla Veena

Orcid: 0000-0002-3611-0143

According to our database1, Pamalla Veena authored at least 12 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
3P-ECLAT: mining partial periodic patterns in columnar temporal databases.
Appl. Intell., January, 2024

2023
A fundamental approach to discover closed periodic-frequent patterns in very large temporal databases.
Appl. Intell., November, 2023

HDSHUI-miner: a novel algorithm for discovering spatial high-utility itemsets in high-dimensional spatiotemporal databases.
Appl. Intell., April, 2023

Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements.
IEEE Access, 2023

Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

Discovering Fuzzy Partial Periodic Patterns in Quantitative Irregular Multiple Time Series.
Proceedings of the IEEE International Conference on Fuzzy Systems, 2023

2022
Discovering Fuzzy Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases.
Proceedings of the IEEE International Conference on Fuzzy Systems, 2022

Towards Efficient Discovery of Periodic-Frequent Patterns in Dense Temporal Databases Using Complements.
Proceedings of the Database and Expert Systems Applications, 2022

2021
A Unified Framework to Discover Partial Periodic-Frequent Patterns in Row and Columnar Temporal Databases.
Proceedings of the 2021 International Conference on Data Mining, 2021

Discovering Fuzzy Frequent Spatial Patterns in Large Quantitative Spatiotemporal databases.
Proceedings of the 30th IEEE International Conference on Fuzzy Systems, 2021

Discovering Top-k Spatial High Utility Itemsets in Very Large Quantitative Spatiotemporal databases.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Discovering Maximal Partial Periodic Patterns in Very Large Temporal Databases.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021


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