Results 141 to 150 of about 2,316 (204)
A survey of streaming data anomaly detection in network security. [PDF]
PeerJ Comput SciZhou P.
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Optimizing an English text reading recommendation model by integrating collaborative filtering algorithm and FastText classification method. [PDF]
HeliyonYan K.
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Time-dependent sequential association rule-based survival analysis: A healthcare application. [PDF]
MethodsXCsalódi R, Bagyura Z, Abonyi J.
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Risk identification and assessment of Internet public opinion on public emergencies based on Bayesian network and association rule mining. [PDF]
Front Public HealthYou M, Pan X, Zhu C.
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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
openaire +3 more sources
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 average-utility itemsets
2009 IEEE International Conference on Systems, Man and Cybernetics, 2009The average utility measure is adopted in this paper to reveal a better utility effect of combining several items than the original utility measure. A mining algorithm is then proposed to efficiently find the high average-utility itemsets. It uses the summation of the maximal utility among the items in each transaction including the target itemset as ...
Tzung-Pei Hong +2 more
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Review on High Utility Itemset Mining Algorithms
Asian Journal of Research in Social Sciences and Humanities, 2016Finding interesting patterns in the database is an important research area in the field of data mining. Association Rule Mining (ARM) finds the items that go together. It finds out the association between items. Frequent Itemset Mining (FIM) finds out the itemset that occur frequently in the database.
V. Kavitha, B. G. Geetha
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High-utility and diverse itemset mining
Applied Intelligence, 2021High-utility Itemset Mining (HUIM) finds patterns from a transaction database with their utility no less than a user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its items. The utility notion enables a data analyst to associate a profit score with each item and thereof to a pattern. We extend the notion of high-
Amit Verma +4 more
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