1 K N P hospital hair O subway 3 3 10 NN 1 0 1 0 2 1 0 4 0 0 0 0 0 0 0 0 2 2 11 0 0 L gyms 2 6 0 0 0 4 0 0 2 6 12 0 1 0
Posted: Wed May 11, 2022 6:12 am
c. Design a two-step iterative
clustering algorithm to reduce inner-cluster bike demand variance
(hint: you may consider the objective of the 2-step iterative
KMeans algorithm). (6 points)
1 K N P hospital hair O subway 3 3 10 NN 1 0 1 0 2 1 0 4 0 0 0 0 0 0 0 0 2 2 11 0 0 L gyms 2 6 0 0 0 4 0 0 2 6 12 0 1 0 5 5 3 0 1 3 12 25 0 6 4 0 1 bus 0 1 0 1 0 0 0 0 2 0 0 1 4 4 M education bar 1 1 0 5 5 0 1 1 0 0 1 1 1 2 0 1 0 3 0 5 2 5 0 4 1 1 0 2 5 6 0 0 3 3 4 4 2 12 1 3 10 1 0 3 0 0 1 2 0 0 WOAH 0 WNA OOM 0 0 1 A B с D E F G Station ID IBike Demand Latitude Longitude restaurant shopping park 72 7.38506452 40.767272 -73.993929 9 0 79 3.357984995 40.719116 -74.006667 10 5 82 1.407835455 40.711174 -74.000165 4 1 83 1.341147741 40.683826 -73.976323 4 5 5 116 10.06550753 40.741776 -74.001497 14 2 119 0.769281183 40.696089 -73.978034 0 0 120 1.213365678 40.686768 -73.959282 4 1 127 11.9611956 40.731724 -74.006744 13 1 128 5.687728938 40.727103 -74.002971 51 6 137 2.362370007 40.761628 -73.972924 10 11 143 2.444180298 40.692395 -73.993379 1 2 144 0.87255946 40.698399 -73.980689 0 1 146 3.695205397 40.71625 -74.009106 19 6 147 4.251592357 40.715422 -74.01122 5 1 150 6.745335427 40.720874 -73.980858 0 1 151 9.732484076 40.722104 -73.997249 44 28 152 2.996288063 40.71474 -74.009106 25 5 153 4.983118538 40.752062 -73.981632 26 3 157 1.920339189 40.690893 -73.996123 14 12 161 7.866572555 40.72917 -73.998102 32 1 164 3.362545186 40.753231 -73.970325 17 1 167 5.955649763 40.748901 -73.976049 31 1 168 8.440514469 40.739713 -73.994564 26 14 173 10.11121412 40.760683 -73.984527 27 9 174 8.054765775 40.738177 -73.977387 0 0 195 4.716920307 40.709056 -74.010434 30 9 212 4.479233227 40.743349 -74.006818 21 7 216 1.039758949 40.700379 -73.995481 1 1 217 1.490212832 40.702772 -73.993836 17 4 223 3.742334141 40.737815 -73.999947 10 1 224 2.74085589 40.711464 -74.005524 5 2 225 3.401273885 40.741951 - 74.00803 17 20 228 2.595608292 40.754601 -73.971879 19 1 229 9.010683169 40.727434 -73.99379 18 19 232 1.386343792 40.695977 -73.990149 0 1 1 0 H massage hotel 5 1 2 1 1 0 0 0 0 1 2 1 0 0 0 2 0 2 4 2 4 0 0 0 0 1 2 2 2 0 0 1 0 3 5 4 0 5 0 0 1 0 0 4 3 1 10 0 3 3 2 2 0 0 7 4 1 1 1 3 0 6 0 2 2 2 0 6 1 0 3 1 0 0 1 1 OO W 2 3 7 21 3 18 2 26 4 1 4 11 2 0 0 0 17 0 0 9912 3 1 이 NO 2 1 1 4 10 24 7 0 0 0 7 0 0 2 17 12 10 10 15 5 7 7 4 21 0 10 9 1 8 5 3 9 9 9 9 9 0 2 0 2 1 0 4 2 9 이이이이 8 18 16 8 4 Oro w 1 0 2 2 3 8 1 0 5 1 1 0 0 0 0 1 2 이 H 1 59 1 11 1 1 11 0 6 0 1 0 1 1 1 2 14 1 1 0 1 6 10 2 2 5 11 0 1 0 4 2 3 4 2 0 5 2 1 1 2 1 4 OON 1 0 0 1 0 1 13 0 Ho 0 OOO OOP 1 2 0 2 3 1 4 1 0 0 0 1 1 0 5 0 4 0 0 1 Bike-POI
clustering algorithm to reduce inner-cluster bike demand variance
(hint: you may consider the objective of the 2-step iterative
KMeans algorithm). (6 points)
1 K N P hospital hair O subway 3 3 10 NN 1 0 1 0 2 1 0 4 0 0 0 0 0 0 0 0 2 2 11 0 0 L gyms 2 6 0 0 0 4 0 0 2 6 12 0 1 0 5 5 3 0 1 3 12 25 0 6 4 0 1 bus 0 1 0 1 0 0 0 0 2 0 0 1 4 4 M education bar 1 1 0 5 5 0 1 1 0 0 1 1 1 2 0 1 0 3 0 5 2 5 0 4 1 1 0 2 5 6 0 0 3 3 4 4 2 12 1 3 10 1 0 3 0 0 1 2 0 0 WOAH 0 WNA OOM 0 0 1 A B с D E F G Station ID IBike Demand Latitude Longitude restaurant shopping park 72 7.38506452 40.767272 -73.993929 9 0 79 3.357984995 40.719116 -74.006667 10 5 82 1.407835455 40.711174 -74.000165 4 1 83 1.341147741 40.683826 -73.976323 4 5 5 116 10.06550753 40.741776 -74.001497 14 2 119 0.769281183 40.696089 -73.978034 0 0 120 1.213365678 40.686768 -73.959282 4 1 127 11.9611956 40.731724 -74.006744 13 1 128 5.687728938 40.727103 -74.002971 51 6 137 2.362370007 40.761628 -73.972924 10 11 143 2.444180298 40.692395 -73.993379 1 2 144 0.87255946 40.698399 -73.980689 0 1 146 3.695205397 40.71625 -74.009106 19 6 147 4.251592357 40.715422 -74.01122 5 1 150 6.745335427 40.720874 -73.980858 0 1 151 9.732484076 40.722104 -73.997249 44 28 152 2.996288063 40.71474 -74.009106 25 5 153 4.983118538 40.752062 -73.981632 26 3 157 1.920339189 40.690893 -73.996123 14 12 161 7.866572555 40.72917 -73.998102 32 1 164 3.362545186 40.753231 -73.970325 17 1 167 5.955649763 40.748901 -73.976049 31 1 168 8.440514469 40.739713 -73.994564 26 14 173 10.11121412 40.760683 -73.984527 27 9 174 8.054765775 40.738177 -73.977387 0 0 195 4.716920307 40.709056 -74.010434 30 9 212 4.479233227 40.743349 -74.006818 21 7 216 1.039758949 40.700379 -73.995481 1 1 217 1.490212832 40.702772 -73.993836 17 4 223 3.742334141 40.737815 -73.999947 10 1 224 2.74085589 40.711464 -74.005524 5 2 225 3.401273885 40.741951 - 74.00803 17 20 228 2.595608292 40.754601 -73.971879 19 1 229 9.010683169 40.727434 -73.99379 18 19 232 1.386343792 40.695977 -73.990149 0 1 1 0 H massage hotel 5 1 2 1 1 0 0 0 0 1 2 1 0 0 0 2 0 2 4 2 4 0 0 0 0 1 2 2 2 0 0 1 0 3 5 4 0 5 0 0 1 0 0 4 3 1 10 0 3 3 2 2 0 0 7 4 1 1 1 3 0 6 0 2 2 2 0 6 1 0 3 1 0 0 1 1 OO W 2 3 7 21 3 18 2 26 4 1 4 11 2 0 0 0 17 0 0 9912 3 1 이 NO 2 1 1 4 10 24 7 0 0 0 7 0 0 2 17 12 10 10 15 5 7 7 4 21 0 10 9 1 8 5 3 9 9 9 9 9 0 2 0 2 1 0 4 2 9 이이이이 8 18 16 8 4 Oro w 1 0 2 2 3 8 1 0 5 1 1 0 0 0 0 1 2 이 H 1 59 1 11 1 1 11 0 6 0 1 0 1 1 1 2 14 1 1 0 1 6 10 2 2 5 11 0 1 0 4 2 3 4 2 0 5 2 1 1 2 1 4 OON 1 0 0 1 0 1 13 0 Ho 0 OOO OOP 1 2 0 2 3 1 4 1 0 0 0 1 1 0 5 0 4 0 0 1 Bike-POI