175 research outputs found
Similarity "Analisa Dampak Investasi Teknologi Informasi Proyek Data Warehouse Pada Perguruan Tinggi Swasta Dengan Metode Simple Roi"
Similarity "Pemodelan Elearning Perguruan Tinggi Dengan Menggunakan Framework Learning Technology System Architecture (Ltsa) Dan Unified Modeling Language (Uml)"
Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns
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 β 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
Similarity "Pengembangan Learning Characteristic Rule Pada Algoritma Data Mining Attribute Oriented Induction"
Similarity "The architecture social media and online newspaper credibility measurement for fake news detection"
Similarity "A Framework to Mine High-Level Emerging Patterns by Attribute-Oriented Induction"
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