Improved BVBUC algorithm to discover closed itemsets in long biological datasets
The task in mining closed frequent itemsets requires the algorithm to mine the frequent ones then determine its closure. The efficiency of closure computation is very important as it will determine the total mining time and the required memory. Over the years, many closure computation methods have b...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Trans Tech Publications Ltd, Switzerland
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/79195/ http://irep.iium.edu.my/79195/ http://irep.iium.edu.my/79195/ http://irep.iium.edu.my/79195/1/79195_Improved%20BVBUC%20Algorithm%20to%20Discover.pdf |
| Summary: | The task in mining closed frequent itemsets requires the algorithm to mine the frequent ones then determine its closure. The efficiency of closure computation is very important as it will determine the total mining time and the required memory. Over the years, many closure computation methods have been proposed to achieve these goals. However, to the best of our knowledge, there is no suitable method that can be adapted for algorithms that enumerate the rowset lattice, which is effective for biological datasets. Therefore, this paper proposed a method for computing closure compare with the method used in BVBUC algorithm method. Finally, BVBUC_I is proposed and the performances of these algorithms were evaluated using two synthetic datasets and three real datasets. The results of these tests proved the efficiency of the proposed method. |
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