A formal series-based unification of the frequent itemset mining approaches
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DOI: 10.1007/s10115-017-1048-y
Abstract: Over the last two decades, a great deal of work has been devoted to the algorithmic aspects of the frequent itemset (FI) mining problem, leading to a phenomenal number of algorithms and associated implementations, each… read more here.
Keywords: itemset mining; mining; frequent itemset; formal series ... See more keywords
Applicable Metamorphic Testing for Erasable-Itemset Mining
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DOI: 10.1109/access.2022.3165656
Abstract: Erasable-itemset mining is often used in production planning to identify itemsets that, if removed, would make little effect on the production profits. Consequently, much recent attention has been focused on increasing the mining efficiency. Yet,… read more here.
Keywords: metamorphic relations; erasable itemset; metamorphic testing; itemset mining ... See more keywords
An Efficient Spark-Based Hybrid Frequent Itemset Mining Algorithm for Big Data
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DOI: 10.3390/data7010011
Abstract: Frequent itemset mining (FIM) is a common approach for discovering hidden frequent patterns from transactional databases used in prediction, association rules, classification, etc. Apriori is an FIM elementary algorithm with iterative nature used to find… read more here.
Keywords: spark; frequent itemset; itemset mining; spark based ... See more keywords
Ignoring Internal Utilities in High-Utility Itemset Mining
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DOI: 10.3390/sym14112339
Abstract: High-utility itemset mining discovers a set of items that are sold together and have utility values higher than a given minimum utility threshold. The utilities of these itemsets are calculated by considering their internal and… read more here.
Keywords: utility values; utility; itemset mining; high utility ... See more keywords