Mining closed high utility itemsets in uncertain databases
| Autor: | Nguyen Hung Bui, Loan T. T. Nguyen, Van-Nam Huynh, Bay Vo, Chun-Wei Lin |
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| Rok vydání: | 2016 |
| Předmět: |
Database
Computer science Probabilistic logic InformationSystems_DATABASEMANAGEMENT Downward closure property 02 engineering and technology computer.software_genre Data mining algorithm ComputingMethodologies_PATTERNRECOGNITION 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining computer |
| Zdroj: | SoICT |
| DOI: | 10.1145/3011077.3011124 |
| Popis: | In order to reduce the number of high-utility itemsets (HUIs), closed high-utility itemsets (CHUIs) have been proposed. However, most techniques for mining CHUIs require certain databases; i.e., there are no probabilities. However, in many real-world applications, an item or itemset may have a probability. Actual data can be affected by the use of noisy sensors. Many algorithms have been proposed to effectively mine frequent itemsets from uncertain databases; however, there are no algorithms for mining CHUIs from uncertain databases. This paper proposes an algorithm called CPHUI-List (closed potential high-utility itemset PEU-List-based mining algorithm) for mining closed potential high-utility itemsets (CPHUIs) from uncertain databases without generating candidates. CPHUI-List performs a depth-first search of the search space, and uses the downward closure property of high transaction-weighed probabilistic and utilization itemsets to prune non-closed potential high-utility itemsets. Experiments show that the runtime and memory consumption of CPHUI-List are lower than those of CHUI-Miner. |
| Databáze: | OpenAIRE |
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