An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm
Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/1942517 |
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| author | Hussein A. Alsaeedi Ahmed S. Alhegami |
| author_facet | Hussein A. Alsaeedi Ahmed S. Alhegami |
| author_sort | Hussein A. Alsaeedi |
| collection | DOAJ |
| description | Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental data over time. In this study, we propose an incremental maximal frequent itemset mining algorithms that integrate subjective interestingness criterion during the process of mining. The proposed framework is designed to deal with incremental data, which usually come at different times. It extends FP-Max algorithm, which is based on FP-Growth method by pushing interesting measures during maximal frequent itemset mining, and performs dynamic and early pruning to leave uninteresting frequent itemsets in order to avoid uninteresting rule generation. The framework was implemented and tested on public databases, and the results found are promising. |
| format | Article |
| id | doaj-art-42680ca9ad96420f8d3f22e9236c1237 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-42680ca9ad96420f8d3f22e9236c12372025-02-03T05:49:23ZengWileyComplexity1099-05262022-01-01202210.1155/2022/1942517An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth AlgorithmHussein A. Alsaeedi0Ahmed S. Alhegami1Department of Computer ScienceFaculty of Computers and Information TechnologyFrequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental data over time. In this study, we propose an incremental maximal frequent itemset mining algorithms that integrate subjective interestingness criterion during the process of mining. The proposed framework is designed to deal with incremental data, which usually come at different times. It extends FP-Max algorithm, which is based on FP-Growth method by pushing interesting measures during maximal frequent itemset mining, and performs dynamic and early pruning to leave uninteresting frequent itemsets in order to avoid uninteresting rule generation. The framework was implemented and tested on public databases, and the results found are promising.http://dx.doi.org/10.1155/2022/1942517 |
| spellingShingle | Hussein A. Alsaeedi Ahmed S. Alhegami An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm Complexity |
| title | An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm |
| title_full | An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm |
| title_fullStr | An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm |
| title_full_unstemmed | An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm |
| title_short | An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm |
| title_sort | incremental interesting maximal frequent itemset mining based on fp growth algorithm |
| url | http://dx.doi.org/10.1155/2022/1942517 |
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