Maswali yanayounganishwa na mwongozo · Kati Kiwango

Maswali ya Kuunganisha ya K-Njia

Fanya mazoezi ya ugawaji wa K-Means, masasisho ya katikati, hali, uanzishaji, na mipaka ya umbo la nguzo kwa mifano thabiti ya kupanga.

Njia za mwongozo zinazohusianaK Inamaanisha Kuunganisha
Swali 1 ya 8

Je, 'K' katika K-Means inarejelea nini?

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  1. Je, 'K' katika K-Means inarejelea nini?
    • Idadi ya makundi unayochagua mapema
    • Idadi ya pointi za data
    • Idadi ya marudio ya mafunzo
    • Idadi ya vipengele katika mkusanyiko wa data
  2. Je, ni hatua gani mbili zinazojirudia katika kitanzi cha K-Means?
    • Gawanya makundi, kisha uunganishe makundi
    • Agiza pointi kwa sentirodi iliyo karibu zaidi, kisha usogeze senti hadi wastani
    • Weka data lebo, kisha ufundishe kiainishaji
    • Panga data, kisha uondoe nje
  3. Je, K-Means inajaribu kupunguza kiasi gani?
    • Idadi ya makundi
    • Umbali kati ya sentimita
    • Umbali wa mraba wa ndani ya nguzo (inertia)
    • Idadi ya vipengele
  4. Kwa nini K-Means inaitwa algoriti 'isiyosimamiwa'?
    • It uses predefined class labels to learn a boundary
    • It requires one label per initial centroid
    • It estimates labels only after supervised training
    • It groups inputs without target class labels
  5. 'Njia ya kiwiko' inayotumiwa sana ni nini?
    • Kuongeza kasi ya mafunzo
    • Kuchagua thamani nzuri ya K
    • Kuanzisha centroids
    • Inaondoa nakala rudufu
  6. Kwa nini K-Means inaweza kutoa matokeo tofauti kwa kukimbia tofauti?
    • K automatically changes on every run
    • Euclidean distance uses a random definition
    • Results depend on initial centroid positions
    • Convergence always produces the same global optimum
  7. A K-Means cluster contains the one-dimensional values 2, 4, and 9. Where does its centroid move in the update step?
    • 5, the mean of the assigned values
    • 4, the middle observed value
    • 9, the largest assigned value
    • 15, the sum of the assigned values
  8. A dataset contains two long, curved groups. Which K-Means limitation should the analyst consider?
    • The groups must have labels before clustering
    • K-Means always discovers curved boundaries exactly
    • Changing K guarantees the intended grouping
    • Euclidean distance to centroids can poorly represent elongated or non-spherical groups