Solve

Re-assignment of data noints to clusters G1 and G2. Clients included in clusters G1 \& G2: G1={ \} G2={ \} Consider yourself as the head of Loan Approval Department at Arabian Commercial Bank (ACB). You realize that not every client is similar and you need to have different strategies to attract different customers. You appreciate the power of customer segmentation to deliver superior results with optimized cost, you are also aware of unsupervised learning techniques such as cluster analysis to create customer segments. You have selected a sample of eight clients with their debt and annual income information. Use k-means clustering to create 2 clusters using the following data: You are expected to perform 3 complete iterations (i.e. find new centroids, after the initial randomly selected centroids). Consider instances 2 and 6 as initial centroids (C1 \& C2 respectively). Additional Instructions: 1. Use appropriate notations for distance calculations 2. Plot the data points (use symbol) on the given girds and identify your centroids (use symbol). 3. Circle your clusters on the grid and mark them as G1 \& G2. 4. Use Manhattan distance. New co-ordinates of C1 : New co-ordinates of C2 : Plot the new two centroids in the chart below Distance Calculations (Manhattan) Distances to C1 Initial Data Plot (Start of 1st Iteration) Initial co-ordinates of C1 : (4.5,2.5) Initial co-ordinates of C2:(2,1.5) Distance Calculations (Manhattan) Distances to C1 D(C1,1)= D(C1,2)= D(C1,3)= D(C1,4)= D(C1,5)= D(C1,6)= D(C1,7)= D(C1,8)= Iteration 2 Calculation of New Centroids New co-ordinates of C1 : New co-ordinates of C2 : ......... Plot the new two centroids in the chart below Distance Calculations (Manhattan) Distances to C1 Re-assignment of data noints to clusters G1 and G2. Clients included in clusters G1 \& G2: G1={ \} Assignment of data points to clusters G1 and G2. Clients included in clusters G1 \& G2: G1={ \} G2={ \} Re-assignment of data noints to clusters G1 and G2. Clients included in clusters G1 \& G2: G1={ \} G2={ \} Consider yourself as the head of Loan Approval Department at Arabian Commercial Bank (ACB). You realize that not every client is similar and you need to have different strategies to attract different customers. You appreciate the power of customer segmentation to deliver superior results with optimized cost, you are also aware of unsupervised learning techniques such as cluster analysis to create customer segments. You have selected a sample of eight clients with their debt and annual income information. Use k-means clustering to create 2 clusters using the following data: You are expected to perform 3 complete iterations (i.e. find new centroids, after the initial randomly selected centroids). Consider instances 2 and 6 as initial centroids (C1 \& C2 respectively). Additional Instructions: 1. Use appropriate notations for distance calculations 2. Plot the data points (use symbol) on the given girds and identify your centroids (use symbol). 3. Circle your clusters on the grid and mark them as G1 \& G2. 4. Use Manhattan distance. New co-ordinates of C1 : New co-ordinates of C2 : Plot the new two centroids in the chart below Distance Calculations (Manhattan) Distances to C1 Initial Data Plot (Start of 1st Iteration) Initial co-ordinates of C1 : (4.5,2.5) Initial co-ordinates of C2:(2,1.5) Distance Calculations (Manhattan) Distances to C1 D(C1,1)= D(C1,2)= D(C1,3)= D(C1,4)= D(C1,5)= D(C1,6)= D(C1,7)= D(C1,8)= Iteration 2 Calculation of New Centroids New co-ordinates of C1 : New co-ordinates of C2 : ......... Plot the new two centroids in the chart below Distance Calculations (Manhattan) Distances to C1 Re-assignment of data noints to clusters G1 and G2. Clients included in clusters G1 \& G2: G1={ \} Assignment of data points to clusters G1 and G2. Clients included in clusters G1 \& G2: G1={ \} G2={ \}