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    Home»Machine Learning»3.1 Klasterlash algoritmlari: K-Means, GMM va DBSCAN | by Eldor Abdukhamidov | Apr, 2025
    Machine Learning

    3.1 Klasterlash algoritmlari: K-Means, GMM va DBSCAN | by Eldor Abdukhamidov | Apr, 2025

    Team_AIBS NewsBy Team_AIBS NewsApril 24, 2025No Comments1 Min Read
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    🔎 Klasterlash (Clustering) — bu Unsupervised Studying yo‘nalishidagi muhim usullardan biri bo‘lib, ma’lumotlar orasidagi yashirin tuzilmalarni topish va ularni o‘xshashlik asosida guruhlarga ajratish vazifasini bajaradi. Bu usulda bizda label (yorliq, y) yo‘q — mannequin o‘zi mustaqil ravishda ma’lumotlarni tahlil qilib, ularni guruhlarga bo‘ladi.

    🧩 Klasterlash nima?

    Klasterlash — bu ma’lumotlar to‘plamini guruhlarga ajratish jarayoni bo‘lib, maqsad — bir guruhdagi elementlar ichki jihatdan bir-biriga o‘xshash, boshqa guruhdagi elementlarga esa farqli bo‘lishidir.

    🎯 Masalan:

    E-tijorat platformasida foydalanuvchilarni:

    • 💸 Budjet xaridorlari
    • 🛒 Tez-tez xarid qiluvchilar
    • 🤑 Ko‘p pul sarflovchilar kabi segmentlarga ajratish mumkin.

    💬 Klasterlash nima uchun kerak?

    • 📊 Ma’lumotlarni soddalashtirish: O‘xshash obyektlarni bitta guruhda ko‘rish orqali umumiy xulosa chiqarish osonlashadi.
    • 📦 Segmentatsiya: Mijozlar, hududlar yoki mahsulotlarni segmentlarga ajratish.
    • 🚨 Anomaliyani aniqlash: Odatdan tashqari (noise) yoki g‘ayrioddiy ma’lumotlarni ajratib ko‘rsatish.
    • 👁 Vizualizatsiya: Ma’lumotlar klasterlar orqali grafik holatda yanada tushunarli bo‘ladi.

    🧠 Unsupervised Learningdagi roli

    Unsupervised Studying algoritmlari orasida klasterlash asosiy o‘rinni egallaydi, chunki bu algoritmlar:

    • 🔍 Ma’lumotlar ichidagi yashirin tuzilmalarni topadi,
    • 🧭 Ma’lumotlarni avtomatik tahlil qiladi,
    • 🎯 Va foydalanuvchi tomonidan belgilanmagan ma’lumotlardan katta foyda olish imkonini beradi.



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