Three-view Synthetic data

10000 instances of three-view numerical data set with 4 clusters and 2 feature components are considered. The data points in each view are generated from a 2-component 2-variate Gaussian mixture model (GMM) where their mixing proportions α₁⁽¹⁾=α₁⁽²⁾=α₁⁽³⁾=α₁⁽⁴⁾=0.3; α₂⁽¹⁾=α₂⁽²⁾=α₂⁽³⁾=α₂⁽⁴⁾=0.15; α₃⁽¹⁾=α₃⁽²⁾=α₃⁽³⁾=α₃⁽⁴⁾=0.15 and α₄⁽¹⁾=α₄⁽²⁾=α₄⁽³⁾=α₄⁽⁴⁾=0.4. The means μᵢₖ⁽¹⁾ for the first view are [-10 -5)],[-9 11], [0 6] and [4 0]; The means μᵢₖ⁽²⁾ for the view 2 are [-8 -12],[-6 -3], [-2 7] and [2 1]; And the means μᵢₖ⁽³⁾ for the third view are [-5 -10],[-8 -1], [0 5] and [5 -4]. The covariance matrices for the three views are Σ₁⁽¹⁾=Σ₁⁽²⁾=Σ₁⁽³⁾=Σ₁⁽⁴⁾=[ [ 1 0; 0 1 ]]; Σ₂⁽¹⁾=Σ₂⁽²⁾=Σ₂⁽³⁾=Σ₂⁽⁴⁾=3 [ [ 1 0; 0 1 ]]; Σ₃⁽¹⁾=Σ₃⁽²⁾=Σ₃⁽³⁾=Σ₃⁽⁴⁾=2 [ [ 1 0; 0 1 ]]; and Σ₄⁽¹⁾=Σ₄⁽²⁾=Σ₄⁽³⁾=Σ₄⁽⁴⁾=0.5 [ [ 1 0; 0 1 ]]. These x₁⁽¹⁾ and x₂⁽¹⁾ are the coordinates for the view 1, x₁⁽²⁾ and x₂⁽²⁾ are the coordinates for the view 2, x₁⁽³⁾ and x₂⁽³⁾ are the coordinates for the view 3. While the original distribution of data points for cluster 1, cluster 2, cluster 3, and cluster 4 are 1514, 3046, 3903, and 1537, respectively.