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Apriori algorithm based prediction of students' mental health risks in the context of artificial intelligence. [PDF]
Front Public HealthFu Y, Ren F, Lin J.
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Revealing the antimicrobial potential of traditional Chinese medicine through text mining and molecular computation. [PDF]
Brief BioinformChung MC, Su LJ, Chen CL, Wu LC.
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Efficient algorithms for deriving complete frequent itemsets from frequent closed itemsets
Applied Intelligence, 2021When mining frequent itemsets (abbr. FIs) from dense datasets, it usually produces too many itemsets and results in the mining task to suffer from a very long execution time and high memory consumption. Frequent closed itemset (abbr. FCI) is a compact and lossless representation of FI. Mining FCIs can not only reduce the execution time and memory usage,
Cheng-Wei Wu +4 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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Frequent Itemset Mining for Big Data
2013 IEEE International Conference on Big Data, 2013Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, recent improvements in the field of parallel programming already provide good tools to tackle this problem.
Moens, Sandy +2 more
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Memory-Aware Frequent k-Itemset Mining
2006In this paper we show that the well known problem of computing frequent k-itemsets (i.e. itemsets of cardinality k) in a given dataset can be reduced to the problem of finding iceberg queries from a stream of queries suitably constructed from the original dataset.
ATZORI M +2 more
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Reference itemsets: useful itemsets to approximate the representation of frequent itemsets
Soft Computing, 2016Deriving frequent itemsets from databases is an important research issue in data mining. The number of frequent itemsets may be unusually large when a low minimum support threshold is given. As such, the design of a compact representation to compress and describe them is an interesting topic. In the past, most related research on compact representation
Jheng-Nan Huang +2 more
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Maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams
2009 IEEE Symposium on Computational Intelligence and Data Mining, 2009Mining frequent itemsets over online data streams, where the new data arrive and the old data will be removed with high speed, is a challenge for the computational complexity. Existing approximate mining algorithms suffer from explosive computational complexity when decreasing the error parameter, ∈, which is used to control the mining accuracy.
Yongyan Wang, Kun Li, Hongan Wang
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Mining Frequent Itemsets from Uncertain Data
2007We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an uncertain data model.
Kao, B, Chui, CK, Hung, E
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Automatic discovery of locally frequent itemsets in the presence of highly frequent itemsets
Intelligent Data Analysis, 2005Many alternatives have been proposed for the mining of association rules involving rare but 'interesting' itemsets in a dataset where there also exist highly frequent itemsets. Nevertheless, all the approaches thus far suggested that we knew which those interesting itemsets are, as well as which is the right support value for them.
Bodon, Ferenc +3 more
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