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- W166509287 abstract "Association rule mining has gathered great attention in recent years due to many important applications. Some influential algorithms have been developed in two categories: (1) candidate-generation-and-test approach such as Apriori [3], (2) patterngrowth approach such as FP-growth [5]. They all suffer from the problem of multiple database scans and setting minimum support thresholds to prune infrequent candidates for process efficiency. We propose a new approach, Fast Support Enumeration (FSE), to mining association rules without the need of predetermining the minimum support threshold. The FSE scans the transaction database only once and enumerates all candidate itemsets with efficient indexing of their support counters that can produce meaningful rules very easily for any item appears at least once in the transactions." @default.
- W166509287 created "2016-06-24" @default.
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- W166509287 date "2008-01-01" @default.
- W166509287 modified "2023-09-26" @default.
- W166509287 title "EFFICIENT SUPPORT COUNTING OF CANDIDATE ITEMSETS FOR ASSOCIATION RULE MINING" @default.
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- W166509287 doi "https://doi.org/10.5220/0001888101800185" @default.
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