An Efficient Method for Mining Closed Potential High-Utility Itemsets
High-utility itemset mining (HUIM) has become a key phase of the pattern mining process, which has wide applications, related to both quantities and profits of items. Many algorithms have been proposed to mine high-utility itemsets (HUIs). Since these algorithms often return a large number of discov...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8999562/ |
| _version_ | 1819276497705238528 |
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| author | Bay Vo Loan T. T. Nguyen Nguyen Bui Trinh D. D. Nguyen Van-Nam Huynh Tzung-Pei Hong |
| author_facet | Bay Vo Loan T. T. Nguyen Nguyen Bui Trinh D. D. Nguyen Van-Nam Huynh Tzung-Pei Hong |
| author_sort | Bay Vo |
| collection | DOAJ |
| description | High-utility itemset mining (HUIM) has become a key phase of the pattern mining process, which has wide applications, related to both quantities and profits of items. Many algorithms have been proposed to mine high-utility itemsets (HUIs). Since these algorithms often return a large number of discovered patterns, a more compact and lossless representation has been proposed. The recently proposed closed high utility itemset mining (CHUIM) algorithms were designed to work with certain types of databases (e.g., those without probabilities). In fact, real-world databases might contain items or itemsets associated with probability values. To effectively mine frequent patterns from uncertain databases, several techniques have been developed, but there does not exist any method for mining CHUIs from this type of databases. This work presents a novel and efficient method without generating candidates, named CPHUI-List, to mine closed potential high-utility itemsets (CPHUIs) from uncertain databases. The proposed algorithm is DFS-based and utilizes the downward closure property of high transaction-weighted probabilistic mining to prune non-CPHUIs. It can be seen from the experiment evaluations that the proposed algorithm has better execution time and memory usage than the CHUI-Miner. |
| first_indexed | 2024-12-23T23:41:10Z |
| format | Article |
| id | doaj.art-443ce42ab9d442c78b41e170434225e6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:41:10Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-443ce42ab9d442c78b41e170434225e62022-12-21T17:25:39ZengIEEEIEEE Access2169-35362020-01-018318133182210.1109/ACCESS.2020.29741048999562An Efficient Method for Mining Closed Potential High-Utility ItemsetsBay Vo0https://orcid.org/0000-0002-2723-1138Loan T. T. Nguyen1https://orcid.org/0000-0001-6440-6462Nguyen Bui2Trinh D. D. Nguyen3Van-Nam Huynh4Tzung-Pei Hong5https://orcid.org/0000-0001-7305-6492Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, VietnamSchool of Computer Science and Engineering, International University, Ho Chi Minh City, VietnamFaculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, VietnamInstitute of Research and Development, Duy Tan University, Da Nang, VietnamSchool of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST), JapanDepartment of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, TaiwanHigh-utility itemset mining (HUIM) has become a key phase of the pattern mining process, which has wide applications, related to both quantities and profits of items. Many algorithms have been proposed to mine high-utility itemsets (HUIs). Since these algorithms often return a large number of discovered patterns, a more compact and lossless representation has been proposed. The recently proposed closed high utility itemset mining (CHUIM) algorithms were designed to work with certain types of databases (e.g., those without probabilities). In fact, real-world databases might contain items or itemsets associated with probability values. To effectively mine frequent patterns from uncertain databases, several techniques have been developed, but there does not exist any method for mining CHUIs from this type of databases. This work presents a novel and efficient method without generating candidates, named CPHUI-List, to mine closed potential high-utility itemsets (CPHUIs) from uncertain databases. The proposed algorithm is DFS-based and utilizes the downward closure property of high transaction-weighted probabilistic mining to prune non-CPHUIs. It can be seen from the experiment evaluations that the proposed algorithm has better execution time and memory usage than the CHUI-Miner.https://ieeexplore.ieee.org/document/8999562/Uncertain databasedata miningknowledge discoveryhigh-utility itemsetclosed high-utility itemset |
| spellingShingle | Bay Vo Loan T. T. Nguyen Nguyen Bui Trinh D. D. Nguyen Van-Nam Huynh Tzung-Pei Hong An Efficient Method for Mining Closed Potential High-Utility Itemsets IEEE Access Uncertain database data mining knowledge discovery high-utility itemset closed high-utility itemset |
| title | An Efficient Method for Mining Closed Potential High-Utility Itemsets |
| title_full | An Efficient Method for Mining Closed Potential High-Utility Itemsets |
| title_fullStr | An Efficient Method for Mining Closed Potential High-Utility Itemsets |
| title_full_unstemmed | An Efficient Method for Mining Closed Potential High-Utility Itemsets |
| title_short | An Efficient Method for Mining Closed Potential High-Utility Itemsets |
| title_sort | efficient method for mining closed potential high utility itemsets |
| topic | Uncertain database data mining knowledge discovery high-utility itemset closed high-utility itemset |
| url | https://ieeexplore.ieee.org/document/8999562/ |
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