Mining Correlated High-Utility Itemsets Using the Bond Measure
| Autor: | Tai Dinh, Philippe Fournier-Viger, Jerry Chun-Wei Lin, Hoai Bac Le |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
ComputingMethodologies_PATTERNRECOGNITION
Computer science Efficient algorithm 020204 information systems Bond 0202 electrical engineering electronic engineering information engineering InformationSystems_DATABASEMANAGEMENT 020201 artificial intelligence & image processing 02 engineering and technology Data mining computer.software_genre computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319320335 HAIS |
| DOI: | 10.1007/978-3-319-32034-2_5 |
| Popis: | Mining high-utility itemsets (HUIs) is the task of finding the sets of items that yield a high profit in customer transaction databases. An important limitation of traditional high-utility itemset mining is that only the utility measure is used for assessing the interestingness of patterns. This leads to finding many itemsets that have a high profit but contain items that are weakly correlated. To address this issue, this paper proposes to integrate the concept of correlation in high-utility itemset mining to find profitable itemsets that are highly correlated, using the bond measure. An efficient algorithm named FCHM (Fast Correlated high-utility itemset Miner) is proposed to efficiently discover correlated high-utility itemsets. Experimental results show that FCHM is highly-efficient and can prune a huge amount of weakly correlated HUIs. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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