K-Zvinoreva Clustering Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Ko iyo 'K' muK-Means inorevei?
   • Nhamba yemasumbu aunosarudza mberi
   • Nhamba ye data points
   • Nhamba yekudzidzira kudzokorora
   • Huwandu hwezvinhu zviri mudhata
2. Ndeapi matanho maviri anodzokorora muK-Means loop?
   • Dimbura masumbu, wozobatanidza masumbu
   • Isa mapoinzi kune ari pedyo secentroid, wozofambisa masendindi kune zvinoreva
   • Nyora data, wozodzidzisa mugadziri
   • Ronga data, wozobvisa kunze
3. Ndeupi huwandu hunoyedzwa neK-Means kudzikisa?
   • Nhamba yemasumbu
   • Chinhambwe chiri pakati pemacentroids
   • Mukati me-cluster squared chinhambwe (inertia)
   • Nhamba yezvinhu
4. Sei K-Nzvimbo ichinzi 'isina kutariswa' algorithm?
   • 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. Chii chinonzi 'elbow method' chinowanzoshandiswa?
   • Kukurumidza kudzidziswa
   • Kusarudza kukosha kwakanaka kweK
   • Kutanga centroids
   • Kubvisa duplicate points
6. Sei K-Means ichigona kupa mhedzisiro dzakasiyana pane dzakasiyana kumhanya?
   • 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