An Efficient Algorithm for Mining Top-K Closed Frequent Itemsets over Data Streams over Data Streams

Mao, Yimin; Xue, Xiaofang; Chen, Jinqing

doi:10.11591/telkomnika.v11i7.2825

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Mining frequent patterns has been done in data stream with DSCL algorithm [18] and Top-K Closed [19]. With frequent pattern we can have strong/sharp discrimination power where have large growth rate and support in target (D2) dataset and other support in contrasting (D1) dataset is small [5][6][7].…”

Section: Mining Frequent Patternmentioning

confidence: 99%

Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns

Warnars¹

2016

IJECE

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Frequent patterns in Attribute Oriented Induction High level Emerging Pattern (AOI-HEP), are recognized when have maximum subsumption target (superset) into contrasting (subset) datasets (contrasting ⊂ target) and having large High Emerging Pattern (HEP) growth rate and support in target dataset. HEP Frequent patterns had been successful mined with AOI-HEP upon 4 UCI machine learning datasets such as adult, breast cancer, census and IPUMS with the number of instances of 48842, 569, 2458285 and 256932 respectively and each dataset has concept hierarchies built from its five chosen attributes. There are 2 and 1 finding frequent patterns from adult and breast cancer datasets, while there is no frequent pattern from census and IPUMS datasets. The finding HEP frequent patterns from adult dataset are adult which have government workclass with an intermediate education (80.53%) and America as native country(33%). Meanwhile, the only 1 HEP frequent pattern from breast cancer dataset is breast cancer which have clump thickness type of AboutAverClump with cell size of VeryLargeSize(3.56%). Finding HEP frequent patterns with AOI-HEP are influenced by learning on high level concept in one of chosen attribute and extended experiment upon adult dataset where learn on marital-status attribute showed that there is no finding frequent pattern.

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Section: Mining Frequent Patternmentioning

confidence: 99%

Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns

Warnars¹

2016

IJECE

View full text Add to dashboard Cite

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“…Frequent pattern is a combination of feature patterns that appear in dataset with frequency not less than a user-specified threshold [1] and the frequent pattern synonym with large pattern was first proposed for market basket analysis in the form of association rules [4]. Mining frequent patterns has been done in data stream with DSCL algorithm [18] and Top-K Closed [19]. With frequent pattern we can have strong/sharp discrimination power where have large growth rate and support in target (D2) dataset and other support in contrasting (D1) dataset is small [5][6][7].…”

Section: Mining Frequent Patternmentioning

confidence: 99%

Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns

Warnars

2016

IJECE

View full text Add to dashboard Cite

show abstract

An Efficient Algorithm for Mining Top-K Closed Frequent Itemsets over Data Streams over Data Streams

Cited by 2 publications

References 6 publications

Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns

Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns

Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns

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