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Business and Consumer Analytics: New Ideas, 2019
We present a survey of the most important algorithms that have been proposed in the context of the frequent itemset mining. We start with an introduction and overview of basic sequential algorithms, and then discuss and compare different parallel approaches based on shared-memory, message-passing, map-reduce, and the use of GPU accelerators.
Cafaro, Massimo, Pulimeno, Marco
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We present a survey of the most important algorithms that have been proposed in the context of the frequent itemset mining. We start with an introduction and overview of basic sequential algorithms, and then discuss and compare different parallel approaches based on shared-memory, message-passing, map-reduce, and the use of GPU accelerators.
Cafaro, Massimo, Pulimeno, Marco
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HBPFP-DC: A parallel frequent itemset mining using Spark
Parallel Computing, 2021The frequent itemset mining (FIM) is one of the most important techniques to extract knowledge from data in many real-world applications. Facing big data applications, parallel and distributed solutions are widely studied.
Yaling Xun +3 more
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Frequent Itemset Mining with Local Differential Privacy
International Conference on Information and Knowledge Management, 2022With the development of the Internet, a large amount of transaction data (e.g., shopping records, web browsing history), which represents user data, has been generated.
Junhui Li +4 more
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FRI-miner: fuzzy rare itemset mining
Applied intelligence (Boston), 2021Data mining is a widely used technology for various real-life applications of data analytics and is important to discover valuable association rules in transaction databases.
Yanling Cui +3 more
semanticscholar +1 more source
2012 IEEE 12th International Conference on Data Mining Workshops, 2012
Frequent Item set Mining (FISM) attempts to find large and frequent item sets in bag-of-items data such as retail market baskets. Such data has two properties that are not naturally addressed by FISM: (i) a market basket might contain items from more than one customer intent(mixture property) and (ii) only a subset of items related to a customer intent
Shailesh Kumar +2 more
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Frequent Item set Mining (FISM) attempts to find large and frequent item sets in bag-of-items data such as retail market baskets. Such data has two properties that are not naturally addressed by FISM: (i) a market basket might contain items from more than one customer intent(mixture property) and (ii) only a subset of items related to a customer intent
Shailesh Kumar +2 more
openaire +1 more source
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016
Frequent itemset mining is one of the most common of data mining tasks. In its simplest form, one is given a table of data in which the columns represent attributes and each row specifies a value for each attribute, each attribute-value pair being referred to as an item.
Hong Huang, Barry O'Sullivan
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Frequent itemset mining is one of the most common of data mining tasks. In its simplest form, one is given a table of data in which the columns represent attributes and each row specifies a value for each attribute, each attribute-value pair being referred to as an item.
Hong Huang, Barry O'Sullivan
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Toward Practical Privacy-Preserving Frequent Itemset Mining on Encrypted Cloud Data
IEEE Transactions on Cloud Computing, 2020Frequent itemset mining, which is the essential operation in association rule mining, is one of the most widely used data mining techniques on massive datasets nowadays.
Shuo Qiu +4 more
semanticscholar +1 more source
Efficient top-k high utility itemset mining on massive data
Information Sciences, 2020In practical applications, top-k high utility itemset mining (top-k HUIM) is an interesting operation to find the k itemsets with the highest utilities. It is analyzed that, the existing algorithms only can deal with the small and medium-sized data, and ...
Xixian Han +3 more
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