Signature-based Tree for Finding Frequent Itemsets
The efficiency of a data mining process depends on the data structure used to find frequent itemsets. Two approaches are possible: use the original transaction dataset or transform it into another more compact structure. Many algorithms use trees as compact structure, like FP-Tree and the associated...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Croatian Communications and Information Society (CCIS)
2023-03-01
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| Series: | Journal of Communications Software and Systems |
| Subjects: | |
| Online Access: | https://jcoms.fesb.unist.hr/10.24138/jcomss-2022-0065/ |
| Summary: | The efficiency of a data mining process depends on the data structure used to find frequent itemsets. Two approaches are possible: use the original transaction dataset or transform it into another more compact structure. Many algorithms use trees as compact structure, like FP-Tree and the associated algorithm FP-Growth. Although this structure reduces the number of scans (only 2), its efficiency depends on two criteria: (i) the size of the support (small or large); (ii) the type of transaction dataset (sparse or dense). But these two criteria can generate very large trees. In this paper, we propose a new tree-based structure that emphasizes on transactions and not on itemsets. Hence, we avoid the problem of support values that have a negative impact on the generated tree. |
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| ISSN: | 1845-6421 1846-6079 |