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Работы, на которые ссылается эта работа

Работ: 41

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

  1. A new approach to classification based on association rule mining

    Guoqing Chen, Hongyan Liu, Yu Lan +2

    Статья2005Цитирований: 7
    ABI

  2. The WEKA data mining software

    Mark Hall, Eibe Frank, Geoffrey Holmes +3

    Статья2009Цитирований: 7
    ABI

  3. CBC: An associative classifier with a small number of rules

    Houtao Deng, George C. Runger, Eugene Tuv +1

    Статья2013Цитирований: 6
    ABI

  4. C4.5: Programs for Machine Learning

    J. R. Quinlan

    Книга1992Цитирований: 6
    ABI

  5. Building an associative classifier with multiple minimum supports

    Li-Yu Hu, Ya‐Han Hu, Chih‐Fong Tsai +2

    Статья2016Цитирований: 6
    ABI

  6. Simple and Accurate Classification Method Based on Class Association Rules Performs Well on Well-Known Datasets

    Jamolbek Mattiev, Branko Kavšek

    Глава2019Цитирований: 6
    ABI

  7. Integrating classification and association rule mining

    Bing Liu, Wynne Hsu, Yiming Ma

    Статья1998Цитирований: 5
    ABI

  8. Finding Groups in Data

    Leonard Kaufman, Peter J. Rousseeuw

    Книга1990Цитирований: 5
    ABI

  9. A comparative study of clustering methods

    Mohamed Zaït, Hammou Messatfa

    Статья1997Цитирований: 5
    ABI

  10. Classification of association rules based on K-means algorithm

    Azzeddine Dahbi, Mohamed Mouhir, Youssef Balouki +1

    Статья2016Цитирований: 5
    ABI

  11. A compact and understandable associative classifier based on overall coverage

    Jamolbek Mattiev, Branko Kavšek

    Статья2020Цитирований: 5
    ABI

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

    Fadi Thabtah, Peter Cowling, Yonghong Peng

    Статья2005Цитирований: 4
    ABI

  13. MCAR: multi-class classification based on association rule

    Fadi Thabtah, Peter Cowling, Yonghong Peng

    Статья2005Цитирований: 4
    ABI

  14. FURIA: an algorithm for unordered fuzzy rule induction

    Jens Hühn, Eyke Hüllermeier

    Статья2009Цитирований: 4
    ABI

  15. Efficient and Effective Clustering Methods for Spatial Data Mining

    Raymond T. Ng, Jiawei Han

    Статья1994Цитирований: 4
    ABI

  16. Distance based clustering of association rules

    Gunjan Gupta, Alexander L. Strehl, Joydeep Ghosh

    Статья1999Цитирований: 4
    ABI

  17. 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
    ABI

  18. Mining Clusters with Association Rules

    Walter A. Kosters, Elena Marchiori, Ard Oerlemans

    Глава1999Цитирований: 4
    ABI

  19. Coverage-Based Classification Using Association Rule Mining

    Jamolbek Mattiev, Branko Kavšek

    СтатьяData Mining Algorithms and ApplicationsApplied Sciences2020Цитирований: 3
    ABI

  20. Combining Naive Bayes and Decision Tables

    Mark A. Hall, Eibe Frank

    Статья2008Цитирований: 3
    ABI

  21. An Optimized Associative Classifier for Incremental Data Based On Non-Trivial Data Insertion

    R. Ramesh, V. Saravanan, R. Manikandan

    Статья2019Цитирований: 3
    ABI

  22. BIRCH

    Tian Zhang, Raghu Ramakrishnan, Miron Livny

    Статья1996Цитирований: 3
    ABI

  23. 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
    ABI

  24. Machine Learning: Proceedings of the Fifteenth International Conference

    Jude Shavlik

    Отчёт1998Цитирований: 2
    ABI

  25. Без названия

    ДругоеЦитирований: 2
    ABI