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International Journal of Information Technology & Decision Making, 2010
High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different items. High utility itemsets mining is useful in decision-making process of many applications, such as retail marketing and Web ...
YING LIU +4 more
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High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different items. High utility itemsets mining is useful in decision-making process of many applications, such as retail marketing and Web ...
YING LIU +4 more
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2009 International Conference on Machine Learning and Cybernetics, 2009
In this paper, we introduce a new kind of mining problem — - mining erasable itemsets, which is de-rived from planning products of the manufacturing industry. For this problem, we first present the formal definition of mining erasable itemsets and discuss some basic properties of the problem.
null Zhi-Hong Deng +3 more
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In this paper, we introduce a new kind of mining problem — - mining erasable itemsets, which is de-rived from planning products of the manufacturing industry. For this problem, we first present the formal definition of mining erasable itemsets and discuss some basic properties of the problem.
null Zhi-Hong Deng +3 more
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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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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
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2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016
We introduce a preferences-based itemset mining framework. Preferences are encoded by a penalty function over the transactions in a database. We define an itemset mining problem where we associate to each transaction a penalty value. This problem consists in generating the frequent itemsets with a maximum penalty threshold.
Jabbour, Said +3 more
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We introduce a preferences-based itemset mining framework. Preferences are encoded by a penalty function over the transactions in a database. We define an itemset mining problem where we associate to each transaction a penalty value. This problem consists in generating the frequent itemsets with a maximum penalty threshold.
Jabbour, Said +3 more
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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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Efficient Skyline Itemsets Mining
Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering - C3S2E '15, 2008Utility Mining (UM) in context of Market Basket Analysis consists of mining itemsets from a transaction database guided by optimizing utility. For example, UM consists of extracting all itemsets in a transaction database having utility above a user-defined minimum threshold or mining Top-K high utility itemset.
Vikram Goyal +2 more
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Third IEEE International Conference on Data Mining, 2004
Traditional association rule mining algorithms only generate a large number of highly frequent rules, but these rules do not provide useful answers for what the high utility rules are. We develop a novel idea of top-K objective-directed data mining, which focuses on mining the top-K high utility closed patterns that directly support a given business ...
null Raymond Chan +2 more
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Traditional association rule mining algorithms only generate a large number of highly frequent rules, but these rules do not provide useful answers for what the high utility rules are. We develop a novel idea of top-K objective-directed data mining, which focuses on mining the top-K high utility closed patterns that directly support a given business ...
null Raymond Chan +2 more
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Mining high occupancy itemsets
Future Generation Computer Systems, 2020Abstract Frequent itemset mining has been extensively studied in data mining for over the last two decades because of its numerous applications. However, the classic support-based mining framework used by most previous studies is not suitable for some real-world applications, such as the travel landscapes recommendation, where o c c u p a n
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