This presentation is a comparison of different clustering based on their computational time. This is the first step in creating open source and bespoke Geodemographic classifications in near real time.
15. Comparing computational efficiency (Z-scores) PAM, and GA on the three geographic aggregations of a dataset covering London. Figure 1: OA (Output Area) level results Figure 2 : LSOA (Lower Super Output Area) level results Figure 3 : Ward level results
16. Comparing computational efficiency (Range Standardisation) PAM, and GA on the three geographic aggregations of a dataset covering London. Figure 4: OA (Output Area) level results Figure 5 : LSOA (Lower Super Output Area) level results Figure 6 : Ward level results
17. Comparing computational efficiency (PCA) PAM, and GA on the three geographic aggregations of a dataset covering London. Figure 7: OA (Output Area) level results Figure 8 : LSOA (Lower Super Output Area) level results Figure 9 : Ward level results
18. Algorithm Stability (w.r.t. Computational time) Figure 10: Running k-means on OA (Output Area) for 120 times on each iteration Figure 11: Running CLARA on OA (Output Area) for 120 times on each iteration Figure 12: Running GA on OA (Output Area) for 120 times on each iteration