Question

please re-write this answer by keyboard don't use handwriting

Answer 1)

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Answer 1

k-mean clustering is cluster of items with the same target category are identified and predicitions for new data items aremade by assuming that type are of the same type as the nearest cluster center.
Implementation
1>place k points into the space represented by the objects that are bing clustered.
2>assign each object to the group that has closest centroid.
3>when all objects have been assigned,recalculate the positions of the k centroids
4>repreat step 2 & 3 until the centroids no longer move

final Baunda shut im

>The image explains the algo works in the cas when k=2.we randomly select the values for μ₁ and μ₂ and the algorithm converges when the mems no longer change (let μ_i=m_i)

>the result depends a lot on the value of k i.e the an actual number of clusters often we have no way of knowing how man clusters exists.
>below picture shows or figure shows what happens when we use k=3(let μ_i=m_i)
sometimes the clustering division turns out to be better at highest k.

stat m 3 Start fanal Boundak Startm

>we can go all the way upto k=n , this proceduce will give us someting know as the nearest
neighbour classifies
>it perform great of the number of feature vector is large,however,computatonally it is much more expresive.