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6 ta ish

Ish: Simple and Accurate Classification Method Based on Class Association Rules Performs Well on Well-Known Datasets

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

    Jamolbek Mattiev, Branko Kavšek

    MaqolaData Mining Algorithms and ApplicationsComputer Science and Information Systems20204 iqtibos
    ABI

  2. Coverage-Based Classification Using Association Rule Mining

    Jamolbek Mattiev, Branko Kavšek

    MaqolaData Mining Algorithms and ApplicationsApplied Sciences20203 iqtibos
    ABI

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

    Jamolbek Mattiev, Branko Kavšek

    BobData Mining Algorithms and ApplicationsLecture notes in computer science20203 iqtibos
    ABI

  4. ACHC: Associative Classifier Based on Hierarchical Clustering

    Jamolbek Mattiev, Branko Kavšek

    BobData Mining Algorithms and ApplicationsLecture notes in computer science20210 iqtibos
    ABI

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

    Jamolbek Mattiev, Christopher Meza, Branko Kavšek

    MaqolaData Mining Algorithms and ApplicationsApplied Sciences20220 iqtibos
    ABI

  6. Importance of Data Preprocessing for Accurate and Effective Prediction of Breast Cancer: Evaluation of Model Performance in Novel Data

    Vekani Baloyi, Jamolbek Mattiev, Sello Mokwena

    MaqolaAI in cancer detectionBig Data and Cognitive Computing20250 iqtibos
    ABI