Matrix-Based Apriori Methods for Frequent Pattern Mining: An In-Depth Survey
- Title
- Matrix-Based Apriori Methods for Frequent Pattern Mining: An In-Depth Survey
- Creator
- Kalpana, P.; Sumathi, P.; Jose, Teena; Deepa, S.; Gondkar, Raju Ramakrishna; Zeema, Loveline J.
- Description
- Data Mining identifies intriguing, useful, and previously unknown patterns and correlations between data stored in databases or warehouses. Frequent Pattern Mining (FPM) is one of the vital methods in the prospering arena of data mining (DM), and it describes the relationship between the items in the datasets. In the last two decades, many studies were carried out in FPM using the Apriori algorithm. The Apriori algorithm requires many database scans and produces numerous candidate itemsets, increasing I/O cost and decreasing computational efficiency. To address these issues, researchers contributed many improved versions of Apriori and proved that those algorithms scan the database only once and identify the frequent itemsets quickly, especially when the itemsets are higher, and provide higher efficiency and feasibility. This research article summarizes matrix-based Apriori algorithms in the literature used for identifying frequent itemsets. 2025 IEEE.
- Source
- 3rd International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2025 - Proceedings;pp.73-78
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Apriori; Data Mining; Frequent Pattern Mining; Matrix-based Apriori
- Coverage
- Kalpana P., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Sumathi P., Dept. of Computer Science, Vysya College, Tamil Nadu, Salem, India; Jose T., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Deepa S., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Gondkar R.R., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Zeema L.J., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833150143-3;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kalpana, P.; Sumathi, P.; Jose, Teena; Deepa, S.; Gondkar, Raju Ramakrishna; Zeema, Loveline J., “Matrix-Based Apriori Methods for Frequent Pattern Mining: An In-Depth Survey,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26091.