Работы, цитирующие эту работу

Работ: 5

Работа: A comparative study of clustering methods

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. CMAC: Clustering Class Association Rules to Form a Compact and Meaningful Associative Classifier

   Jamolbek Mattiev, Branko Kavšek

   ГлаваData Mining Algorithms and ApplicationsLecture notes in computer science2020Цитирований: 3

   ABIABI:AkademIndex/openalex/2026.other.468606

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. ACHC: Associative Classifier Based on Hierarchical Clustering

   Jamolbek Mattiev, Branko Kavšek

   ГлаваData Mining Algorithms and ApplicationsLecture notes in computer science2021Цитирований: 0

   ABIABI:AkademIndex/openalex/2021.chapter.000095

5. 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