NUCLEAR: An Efficient Method for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets
Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then extract the FIs for each CFI. However, some algorithms require the generators for each CFI in order to extract...
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Information Technology in Industry
2023
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| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/handle/123456789/2710 |
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oai:scholar.dlu.edu.vn:123456789-27102023-06-14T17:05:11Z NUCLEAR: An Efficient Method for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets Phạm, Quang Huy Dương, Bảo Ninh Philippe, Fournier-Viger Duc, Tran Alioune, Ngom frequent itemset mining generators closed frequent itemsets Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then extract the FIs for each CFI. However, some algorithms require the generators for each CFI in order to extract the FIs, leading to an extra cost. In this paper, we introduce an effective algorithm, called NUCLEAR, which can induce the FIs from the lattice of CFIs without the need of generators. It can enumerate generators as well by a similar fashion. Experimental results showed that NUCLEAR is effective as compared to previous studies, especially, the time for extracting the FIs is usually much smaller than that for mining the CFIs. 7 2 2023-06-14T17:05:03Z 2023-06-14T17:05:03Z 2019 Journal article Bài báo đăng trên tạp chí quốc tế (có ISSN), bao gồm book chapter https://scholar.dlu.edu.vn/handle/123456789/2710 10.17762/itii.v7i2.65 en Information Technology in Industry Information Technology in Industry |
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Thư viện Trường Đại học Đà Lạt |
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Thư viện số |
| language |
English |
| topic |
frequent itemset mining generators closed frequent itemsets |
| spellingShingle |
frequent itemset mining generators closed frequent itemsets Phạm, Quang Huy Dương, Bảo Ninh Philippe, Fournier-Viger Duc, Tran Alioune, Ngom NUCLEAR: An Efficient Method for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets |
| description |
Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then extract the FIs for each CFI. However, some algorithms require the generators for each CFI in order to extract the FIs, leading to an extra cost. In this paper, we introduce an effective algorithm, called NUCLEAR, which can induce the FIs from the lattice of CFIs without the need of generators. It can enumerate generators as well by a similar fashion. Experimental results showed that NUCLEAR is effective as compared to previous studies, especially, the time for extracting the FIs is usually much smaller than that for mining the CFIs. |
| format |
Journal article |
| author |
Phạm, Quang Huy Dương, Bảo Ninh Philippe, Fournier-Viger Duc, Tran Alioune, Ngom |
| author_facet |
Phạm, Quang Huy Dương, Bảo Ninh Philippe, Fournier-Viger Duc, Tran Alioune, Ngom |
| author_sort |
Phạm, Quang Huy |
| title |
NUCLEAR: An Efficient Method for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets |
| title_short |
NUCLEAR: An Efficient Method for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets |
| title_full |
NUCLEAR: An Efficient Method for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets |
| title_fullStr |
NUCLEAR: An Efficient Method for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets |
| title_full_unstemmed |
NUCLEAR: An Efficient Method for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets |
| title_sort |
nuclear: an efficient method for mining frequent itemsets and generators from closed frequent itemsets |
| publisher |
Information Technology in Industry |
| publishDate |
2023 |
| url |
https://scholar.dlu.edu.vn/handle/123456789/2710 |
| _version_ |
1778233930582851584 |