K-Means Clustering | K-Means++ Clustering | Cluster Analysis | Data Science

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  • Опубликовано: 24 ноя 2024

Комментарии • 11

  • @santoshpoudel1853
    @santoshpoudel1853 10 месяцев назад +1

    amazing🔥🔥🔥🔥

  • @SIDDHARTHSINGH-o2s
    @SIDDHARTHSINGH-o2s 2 месяца назад

    Amazing Explanation!

  • @manigoyal4872
    @manigoyal4872 10 месяцев назад

    A very precise and easy to understand explanations
    Thank you for the video

  • @SamuelOgazi
    @SamuelOgazi 7 месяцев назад

    This was quite interesting.
    Thank you!

  • @mechdoudmohammed6558
    @mechdoudmohammed6558 5 месяцев назад

    It's a good explanation, thanks.

    • @prosmartanalytics
      @prosmartanalytics  5 месяцев назад

      Thank you! We are glad you found it useful. 👍

  • @aswinimechiri3157
    @aswinimechiri3157 7 месяцев назад

    what is the best way to choose initial centroid points?

    • @prosmartanalytics
      @prosmartanalytics  7 месяцев назад

      Though starting randomly for the first custer center is ok, but therafter for subsequent cluster centers we would vote in favor of the logic used by kmeans++.

  • @Abdulmoiz-lx8vv
    @Abdulmoiz-lx8vv 5 месяцев назад

    boss