Results 121 to 130 of about 719,228 (260)
Association rules recommendation algorithm supporting recommendation nonempty
Tongxin xuebao, 2017 Existing association rule recommendation technologies were focus on extraction efficiency of association rule in data mining.However,it lacked consideration of recommendation balance between popular and unusual data and efficient processing.In order to ...
Ming HE, Wei-shi LIU, Jiang ZHANG
doaj +2 more sources
A NOVEL ALGORITHM FOR ASSOCIATION RULE MINING FROM DATA WITH INCOMPLETE AND MISSING VALUES [PDF]
ICTACT Journal on Soft Computing, 2011 Missing values and incomplete data are a natural phenomenon in real datasets. If the association rules mine incomplete disregard of missing values, mistaken rules are derived.
K. Rameshkumar
doaj
Engineering ProceedingsHigh-utility itemset mining (HUIM) utilizes the threshold value to extract HUI from the transactional database. However, it is difficult to define an optimal threshold value, since it depends on the domain knowledge of the application.
Ye-In Chang +4 more
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Temporal Erasable Itemset Mining Algorithms with Lower-Bound Strategies
, 2022 Erasable itemset mining is a valuable research topic for manufacturers. It identifies less profitable materials in a product dataset to assist in managerial decision-making, facilitating better trade-offs between manufacture and procurement.
Li, Jia-Xiang
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On Minimal Infrequent Itemset Mining
, 2008 —A new algorithm for minimal infrequent itemset mining is presented. Potential applications of finding infrequent itemsets include statistical disclosure risk assessment, bioinformatics, and fraud detection.
core
Fault localization using itemset mining under constraints
, 2017 International audienceWe introduce in this paper an itemset mining approach to tackle the fault localization problem, which is one of the most difficult processes in software debug- ging.
Maamar, Mehdi +3 more
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Scalable out-of-core itemset mining
, 2015 Itemset mining looks for correlations among data items in large transactional datasets. Traditional in-core mining algorithms do not scale well with huge data volumes, and are hindered by critical issues such as long execution times due to massive ...
CHIUSANO, SILVIA ANNA +3 more
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Jurnal Informatika, 2007 Taking attendance from employees always becomes a problem for Human Resource Department (HRD) in many companies lately. Although there is an automatic check-lock machine, it still has a weakness.
Gregorius Satia Budhi +1 more
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Frequent Itemset Mining in Large Datasets a Survey
, 2017 Frequent Itemset Mining is a well-known area in data mining. Most of the techniques available for frequent itemset mining requires complete information about the data which can result in generation of the association rules.
Manish Kumar, Amrit Pal
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