Работы, цитирующие эту работу

Работ: 4

Работа: MMAC: A New Multi-Class, Multi-Label Associative Classification Approach

1. Distance based clustering of class association rules to build a compact, accurate and descriptive classifier

   Jamolbek Mattiev, Branko Kavšek

   СтатьяData Mining Algorithms and ApplicationsComputer Science and Information Systems2020Цитирований: 4

   ABIABI:AkademIndex/openalex/2026.other.416363

2. Coverage-Based Classification Using Association Rule Mining

   Jamolbek Mattiev, Branko Kavšek

   СтатьяData Mining Algorithms and ApplicationsApplied Sciences2020Цитирований: 3

   ABIABI:AkademIndex/openalex/2020.article.000232

3. ACMKC: A Compact Associative Classification Model Using K-Modes Clustering with Rule Representations by Coverage

   Jamolbek Mattiev, Monte Davityan, Branko Kavšek

   СтатьяData Mining Algorithms and ApplicationsMathematics2023Цитирований: 1

   ABIABI:AkademIndex/openalex/2023.article.001595

4. The Effect of “Directness” of the Distance Metric to Produce Compact and Accurate Associative Classification Models

   Jamolbek Mattiev, Christopher Meza, Branko Kavšek

   СтатьяData Mining Algorithms and ApplicationsApplied Sciences2022Цитирований: 0

   ABIABI:AkademIndex/openalex/2022.article.002421