based on the distances to existing centers. The probability of selecting a point as a new center is proportional to its distance from the already determined centers, squared.Another limitation is the assumption of spherical and isotropic data clusters in Euclidean space, which doesn't a...
Building on the idea both that differences in score level as differences in score covariances may indicate the presence of clusters and that these level and covariance differences may pertain to different variables, subspace K-means rests on the key assumption that the cluster centroids and the ...
2. [10 marks; ~0.5 hrs] K-means is derived from EM. However, instead of calculating both means and variances, it calculates only the means. What is(are) the assumption(s) that K-Means makes? Using a suitable synthetic dataset, demonstrate how K-Means fails when any one of theassumpti...
and can be applied as long as the assumption for its use 𝜏≪𝐿2𝐷𝑠τ≪L2Ds is satisfied [43,44]. In Equation (3), L represents the characteristic diffusion length equal to 𝑅𝑠/3Rs/3 for spherical-like active material particles as the ones of NVPF [45,46], 𝜏τ ...