We begin by exploring a data set that exemplifies a consumer segmentation project.
For this example, suppose you work for a subscription-based service (such as cable television or membership in a warehouse club) and have collected data from N = 300 respondents on age, gender, income, number of children, whether they own or rent their homes, and whether they currently subscribe to the offered service or not.
library(cluster)
require(caret)
require(mclust)
library(psych)
library(randomForest)
setwd("C:\\Users\\armop\\Dropbox\\PHD\\Teaching\\AGBU505\\2019\\Unsupervised Learning\\Lecture 6")
load("seg_df.RData")
tail(seg.df)
age | gender | income | kids | ownHome | subscribe | |
---|---|---|---|---|---|---|
295 | 36.14964 | Male | 40522.39 | 0 | ownYes | subNo |
296 | 32.95227 | Female | 43882.43 | 0 | ownYes | subNo |
297 | 40.96255 | Female | 64197.09 | 2 | ownNo | subNo |
298 | 38.22980 | Male | 47580.93 | 0 | ownNo | subYes |
299 | 33.17036 | Male | 60747.34 | 1 | ownNo | subNo |
300 | 34.38388 | Male | 53674.93 | 5 | ownYes | subNo |
summary(seg.df)
age gender income kids ownHome Min. :19.26 Female:157 Min. : -5183 Min. :0.00 ownNo :159 1st Qu.:33.01 Male :143 1st Qu.: 39656 1st Qu.:0.00 ownYes:141 Median :39.49 Median : 52014 Median :1.00 Mean :41.20 Mean : 50937 Mean :1.27 3rd Qu.:47.90 3rd Qu.: 61403 3rd Qu.:2.00 Max. :80.49 Max. :114278 Max. :7.00 subscribe subNo :260 subYes: 40
We use the subscription segment data in this chapter for two purposes: to examine clustering methods that find intrinsic groupings (unsupervised learning), and to show how classification methods learn to predict group membership from known cases (supervised learning).
Clustering analysis requires two stages: finding a proposed cluster solution and evaluating that solution for one’s business needs. For each method we go through the following steps:
• Transform the data if needed for a particular clustering method; for instance,
some methods require all numeric data (e.g., kmeans(), mclust()) or all
categorical data (e.g., poLCA()).
• Compute a distance matrix if needed; some methods require a precomputed
matrix of similarity in order to group observations (e.g., hclust()) .
• Apply the clustering method and save its result to an object. For some methods
this requires specifying the number (K) of groups desired (e.g., kmeans(),
poLCA()).
• For some methods, further parse the object to obtain a solution with K groups
(e.g., hclust()).
• Examine the solution in the model object with regard to the underlying data,
and consider whether it answers a business question.
Recommendation: Think hard about how you would know whether the solution—assignments of observations to groups—that is proposed by a clustering method is useful for your business problem. Just because some grouping is proposed by an algorithm does not mean that it will help your business.
The primary information in hierarchical clustering is the distance between observations. There are many ways to compute distance, and we start by examining the best-known method, the Euclidean distance.
A limitation is that Euclidean distance is only defined when observations are numeric. In our data seg.df it is impossible to compute the distance between Male and Female (a fact many people suspect even before studying statistics). If we did not care about the factor variables, then we could compute Euclidean distance using only the numeric columns.
For example, we can select the three numeric columns in seg.df, calculate the distances, and then look at a matrix for just the first five observations as follows:
d <- dist(seg.df[, c("age", "income", "kids")])
dist(seg.df[, c("age", "income", "kids")])
1 2 3 4 5 2 1.393653e+04 3 5.313626e+03 8.622906e+03 4 3.155918e+04 4.549570e+04 3.687280e+04 5 2.987020e+04 4.380673e+04 3.518383e+04 1.688977e+03 6 8.660554e+03 2.259708e+04 1.397418e+04 2.289862e+04 2.120965e+04 7 3.020058e+04 1.626406e+04 2.488696e+04 6.175976e+04 6.007078e+04 8 2.237652e+03 1.169895e+04 3.076087e+03 3.379675e+04 3.210778e+04 9 1.150295e+03 1.278624e+04 4.163334e+03 3.270947e+04 3.102049e+04 10 3.085104e+03 1.702160e+04 8.398708e+03 2.847410e+04 2.678512e+04 11 1.565249e+03 1.237129e+04 3.748379e+03 3.312442e+04 3.143545e+04 12 1.628467e+04 3.022119e+04 2.159829e+04 1.527451e+04 1.358554e+04 13 1.985485e+04 3.379137e+04 2.516847e+04 1.170433e+04 1.001536e+04 14 7.717336e+03 2.165386e+04 1.303096e+04 2.384184e+04 2.215287e+04 15 2.383050e+03 1.155349e+04 2.930590e+03 3.394221e+04 3.225324e+04 16 7.554029e+03 2.149055e+04 1.286765e+04 2.400515e+04 2.231618e+04 17 3.644253e+03 1.758077e+04 8.957873e+03 2.791493e+04 2.622596e+04 18 8.228007e+03 5.708530e+03 2.914382e+03 3.978718e+04 3.809821e+04 19 1.404567e+04 2.798219e+04 1.935929e+04 1.751351e+04 1.582454e+04 20 3.166762e+03 1.710325e+04 8.480358e+03 2.839245e+04 2.670348e+04 21 1.285003e+03 1.265153e+04 4.028625e+03 3.284418e+04 3.115521e+04 22 3.319446e+03 1.061710e+04 1.994217e+03 3.487860e+04 3.318963e+04 23 5.330906e+03 8.605643e+03 2.153216e+01 3.689006e+04 3.520109e+04 24 6.645204e+03 2.058172e+04 1.195882e+04 2.491398e+04 2.322501e+04 25 1.303709e+04 2.697360e+04 1.835071e+04 1.852210e+04 1.683313e+04 26 1.713172e+03 1.564968e+04 7.026783e+03 2.984602e+04 2.815705e+04 27 9.958085e+03 2.389459e+04 1.527170e+04 2.160111e+04 1.991215e+04 28 7.879781e+03 2.181631e+04 1.319340e+04 2.367940e+04 2.199042e+04 29 6.629324e+03 2.056583e+04 1.194294e+04 2.492986e+04 2.324089e+04 30 2.700589e+04 4.094241e+04 3.231951e+04 4.553287e+03 2.864315e+03 31 1.623462e+04 3.017115e+04 2.154825e+04 1.532456e+04 1.363559e+04 32 4.307456e+03 9.629075e+03 1.006174e+03 3.586663e+04 3.417766e+04 33 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5.937499e+03 77 3.403748e+03 1.053278e+04 1.909878e+03 3.496292e+04 3.327395e+04 78 1.052586e+04 2.446238e+04 1.583948e+04 2.103332e+04 1.934435e+04 79 1.920533e+02 1.412850e+04 5.505602e+03 3.136720e+04 2.967823e+04 80 3.329517e+02 1.426938e+04 5.646486e+03 3.122632e+04 2.953734e+04 81 3.006139e+04 4.399791e+04 3.537501e+04 1.497827e+03 1.913184e+02 82 2.806261e+03 1.674277e+04 8.119871e+03 2.875293e+04 2.706396e+04 83 1.744992e+04 3.138644e+04 2.276354e+04 1.410926e+04 1.242029e+04 84 1.367323e+02 1.380041e+04 5.177522e+03 3.169529e+04 3.000631e+04 85 1.679219e+04 3.072871e+04 2.210581e+04 1.476699e+04 1.307802e+04 86 1.004010e+04 3.896432e+03 4.726474e+03 4.159927e+04 3.991030e+04 87 2.479975e+03 1.145656e+04 2.833651e+03 3.403915e+04 3.235018e+04 88 7.143679e+03 6.792858e+03 1.830060e+03 3.870285e+04 3.701388e+04 89 9.388666e+03 2.332519e+04 1.470229e+04 2.217051e+04 2.048154e+04 90 2.670186e+04 4.063838e+04 3.201548e+04 4.857322e+03 3.168350e+03 91 1.945439e+04 3.339091e+04 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108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 4.964993e+03 288 1.297399e+04 8.008996e+03 289 1.850035e+04 2.346534e+04 3.147433e+04 290 1.219926e+03 3.745072e+03 1.175406e+04 1.972027e+04 291 1.168346e+04 1.664845e+04 2.465745e+04 6.816891e+03 1.290338e+04 292 1.354800e+04 8.583009e+03 5.740160e+02 3.204834e+04 1.232808e+04 293 4.193066e+03 9.158057e+03 1.716705e+04 1.430728e+04 5.412990e+03 294 2.387597e+03 2.577398e+03 1.058639e+04 2.088794e+04 1.167688e+03 295 1.421789e+04 9.252896e+03 1.243902e+03 3.271823e+04 1.299796e+04 296 1.085786e+04 5.892864e+03 2.116136e+03 2.935820e+04 9.637934e+03 297 9.456808e+03 1.442180e+04 2.243080e+04 9.043548e+03 1.067673e+04 298 7.159353e+03 2.194363e+03 5.814635e+03 2.565970e+04 5.939430e+03 299 6.007061e+03 1.097205e+04 1.898104e+04 1.249329e+04 7.226987e+03 300 1.065376e+03 3.899646e+03 1.190864e+04 1.956569e+04 1.548358e+02 291 292 293 294 295 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 2.523146e+04 293 7.490395e+03 1.774106e+04 294 1.407105e+04 1.116041e+04 6.580659e+03 295 2.590135e+04 6.698998e+02 1.841095e+04 1.183029e+04 296 2.254131e+04 2.690151e+03 1.505092e+04 8.470261e+03 3.360035e+03 297 2.226658e+03 2.300481e+04 5.263743e+03 1.184440e+04 2.367470e+04 298 1.884281e+04 6.388648e+03 1.135242e+04 4.771760e+03 7.058535e+03 299 5.676403e+03 1.955506e+04 1.814004e+03 8.394650e+03 2.022494e+04 300 1.274881e+04 1.248265e+04 5.258416e+03 1.322252e+03 1.315254e+04 296 297 298 299 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 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head(seg.df)
age | gender | income | kids | ownHome | subscribe |
---|---|---|---|---|---|
47.31613 | Male | 49482.81 | 2 | ownNo | subNo |
31.38684 | Male | 35546.29 | 1 | ownYes | subNo |
43.20034 | Male | 44169.19 | 0 | ownYes | subNo |
37.31700 | Female | 81041.99 | 1 | ownNo | subNo |
40.95439 | Female | 79353.01 | 3 | ownYes | subNo |
43.03387 | Male | 58143.36 | 4 | ownYes | subNo |
as.matrix(d)[1:6,1:6]
1 | 2 | 3 | 4 | 5 | 6 | |
---|---|---|---|---|---|---|
1 | 0.000 | 13936.531 | 5313.626 | 31559.178 | 29870.205 | 8660.554 |
2 | 13936.531 | 0.000 | 8622.906 | 45495.698 | 43806.727 | 22597.078 |
3 | 5313.626 | 8622.906 | 0.000 | 36872.800 | 35183.828 | 13974.178 |
4 | 31559.178 | 45495.698 | 36872.800 | 0.000 | 1688.977 | 22898.624 |
5 | 29870.205 | 43806.727 | 35183.828 | 1688.977 | 0.000 | 21209.651 |
6 | 8660.554 | 22597.078 | 13974.178 | 22898.624 | 21209.651 | 0.000 |
For seg.df we cannot assume that factor variables are irrelevant to our cluster definitions; it is better to use all the data. The daisy() function in the cluster package works with mixed data types by rescaling the values, so we use that instead of Euclidean distance:
seg.dist <- daisy(seg.df)
as.matrix(seg.dist)[1:6,1:6]
1 | 2 | 3 | 4 | 5 | 6 | |
---|---|---|---|---|---|---|
1 | 0.0000000 | 0.2532815 | 0.2329028 | 0.2617250 | 0.4161338 | 0.2380254 |
2 | 0.2532815 | 0.0000000 | 0.0679978 | 0.4129493 | 0.3014468 | 0.1346597 |
3 | 0.2329028 | 0.0679978 | 0.0000000 | 0.4246012 | 0.2932957 | 0.1151873 |
4 | 0.2617250 | 0.4129493 | 0.4246012 | 0.0000000 | 0.2265436 | 0.4522710 |
5 | 0.4161338 | 0.3014468 | 0.2932957 | 0.2265436 | 0.0000000 | 0.2257274 |
6 | 0.2380254 | 0.1346597 | 0.1151873 | 0.4522710 | 0.2257274 | 0.0000000 |
The distances look reasonable (zeroes on the diagonal, symmetric, scaled [0, 1]) so we proceed to the hierarchical cluster method itself, invoking hclust() on the dissimilarity matrix:
seg.hc <- hclust(seg.dist, method="complete")
We use the complete linkage method, which evaluates the distance between every member when combining observations and groups.
A simple call to plot() will draw the hclust object:
plot(seg.hc)
A hierarchical dendrogram is interpreted primarily by height and where observations are joined. The height represents the dissimilarity between elements that are joined. At the lowest level of the tree in figure we see that elements are combined into small groups of 2–10 that are relatively similar, and then those groups are successively combined with less similar groups moving up the tree. The horizontal ordering of branches is not important; branches could exchange places with no change in interpretation.
Figure is difficult to read, so it is helpful to zoom in on one section of the chart. We can cut it at a specified location and plot just one branch as follows.We coerce it to a dendrogram object (as.dendrogram(. . . )), cut it at a certain height (h=. . . ), and select the resulting branch that we want (. . . lower[[1]]).
plot(cut(as.dendrogram(seg.hc), h=0.5)$lower[[2]])#$lower[[1]]
cut(as.dendrogram(seg.hc), h=0.5)
$upper 'dendrogram' with 2 branches and 5 members total, at height 0.8111076 $lower $lower[[1]] 'dendrogram' with 2 branches and 22 members total, at height 0.3573151 $lower[[2]] 'dendrogram' with 2 branches and 18 members total, at height 0.4727599 $lower[[3]] 'dendrogram' with 2 branches and 136 members total, at height 0.4660957 $lower[[4]] 'dendrogram' with 2 branches and 58 members total, at height 0.3162601 $lower[[5]] 'dendrogram' with 2 branches and 66 members total, at height 0.3417087
The result is shown in figure above, where we are now able to read the observation labels (which defaults to the row names—usually the row numbers—of observations in the data frame). Each node at the bottom represents one customer, and the brackets show how each has been grouped progressively with other customers.
We can check the similarity of observations by selecting a few rows listed in figure. Observations $101$ and $107$ are represented as being quite similar because they are linked at a very low height, as are observations $278$ and $294$. On the other hand, observations $173$ and $141$ are only joined at the highest level of this branch and thus should be relatively dissimilar. We can check those directly:
seg.df[c(101, 107), ] # similar
age | gender | income | kids | ownHome | subscribe | |
---|---|---|---|---|---|---|
101 | 24.73796 | Male | 18457.85 | 1 | ownNo | subYes |
107 | 23.19013 | Male | 17510.28 | 1 | ownNo | subYes |
seg.df[c(278, 294), ] # similar
age | gender | income | kids | ownHome | subscribe | |
---|---|---|---|---|---|---|
278 | 36.23860 | Female | 46540.88 | 1 | ownNo | subYes |
294 | 35.79961 | Female | 52352.69 | 1 | ownNo | subYes |
seg.df[c(173, 141), ] # less similar
age | gender | income | kids | ownHome | subscribe | |
---|---|---|---|---|---|---|
173 | 64.70641 | Male | 45517.15 | 0 | ownNo | subYes |
141 | 25.17703 | Female | 20125.80 | 2 | ownNo | subYes |
The first two sets—observations that are neighbors in the dendrogram—are similar on all variables (age, gender, income, etc.). The third set—observations taken from widely separated branches—differs substantially on the first four variables.
Finally, we might check one of the goodness-of-fit metrics for a hierarchical cluster solution. One method is the cophenetic correlation coefficient (CPCC), which assesses how well a dendrogram (in this case seg.hc) matches the true distance metric (seg.dist).
cor(cophenetic(seg.hc), seg.dist)
CPCC is interpreted similarly to Pearson’s r. In this case, CPCC > 0.7 indicates a relatively strong fit, meaning that the hierarchical tree represents the distances between customers well.
How do we get specific segment assignments? A dendrogram can be cut into clusters at any height desired, resulting in different numbers of groups. For instance, if dendogram is cut at a height of $0.7$, there are $K = 2$ groups (draw a horizontal line at $0.7$ and count how many branches it intersects; each cluster below is a group), while cutting at height of $0.4$ defines $K = 7$ groups.
Because a dendrogram can be cut at any point, the analyst must specify the number of groups desired. We can see where the dendrogram would be cut by overlaying its plot() with rect.hclust(), specifying the number of groups we want (k=. . . ):
plot(seg.hc)
rect.hclust(seg.hc, k=4, border="blue")
We obtain the assignment vector for observations using cutree():
seg.hc.segment <- cutree(seg.hc, k=4) # membership vector for 4 groups
table(seg.hc.segment)
seg.hc.segment 1 2 3 4 124 136 18 22
We see that groups 1 and 2 dominate the assignment. Note that the class labels $(1, 2, 3, 4)$ are in arbitrary order and are not meaningful in themselves. seg.hc.segment is the vector of group assignments.
A segment inspection function may be complex depending on the business need and might even include plotting as well as data summarization. For purposes here we use a simple function that reports the mean by group. We use mean here instead of a more robust metric such as median because we have several binary variables and mean() easily shows the mixture proportion for them (i.e., 1.5 means a 50% mix of 1 and 2). A very simple function is:
seg.summ <- function(data, groups) {
aggregate(data, list(groups), function(x) mean(as.numeric(x)))
}
This function first splits the data by reported group (aggregate(. . ., list (groups), . . . )). An anonymous function (function(x) . . . ) then converts all of a group’s data to numeric (as.numeric(x)) and computes its mean().
seg.summ(seg.df, seg.hc.segment)
Group.1 | age | gender | income | kids | ownHome | subscribe |
---|---|---|---|---|---|---|
1 | 40.78456 | 2.000000 | 49454.08 | 1.314516 | 1.467742 | 1 |
2 | 42.03492 | 1.000000 | 53759.62 | 1.235294 | 1.477941 | 1 |
3 | 44.31194 | 1.388889 | 52628.42 | 1.388889 | 2.000000 | 2 |
4 | 35.82935 | 1.545455 | 40456.14 | 1.136364 | 1.000000 | 2 |
We see that groups 1 and 2 are distinct from 3 and 4 due to subscription status. Among those who do not subscribe, group 1 is all male (gender=2 as in levels(seg.df$gender)) while group 2 is all female. Subscribers are differentiated into those who own a home (group 3) or not (group 4).
Important:Is this interesting from a business point of view? Probably not. Imagine describing the results to a set of executives: “Our advanced hierarchical analysis in R examined consumers who don’t yet subscribe and found two segments to target! The segments are known as ‘Men’ and ‘Women.”’ Such insight is unlikely to win the analyst a promotion.
We confirm this with a quick plot of gender by subscribe with all of the observations colored by segment membership. To do this, we use a trick: we convert the factor variables to numeric, and call the jitter() function to add a bit of noise and prevent all the cases from being plotted at the same positions (namely at exactly four points: (1,1), (1,2), (2,1), and (2,2)). We color the points by segment with col=seg.hc.segment, and label the axes with more meaningful labels:
plot(jitter(as.numeric(seg.df$gender)) ∼
jitter(as.numeric(seg.df$subscribe)),
col=seg.hc.segment, yaxt="n", xaxt="n", ylab="", xlab="")
axis(1, at=c(1, 2), labels=c("Subscribe: No", "Subscribe: Yes"))
axis(2, at=c(1, 2), labels=levels(seg.df$gender))
The resulting plot is shown in figure, where we see clearly that the nonsubscribers are broken into two segments (colored red and black) that are perfectly correlated with gender.
Note:Such a plot is a quick hack, which we suggest only for rapid inspection and debugging purposes.
Important: Why did hclust() find a result that is so uninteresting? That may be answered in several ways. For one thing, machine learning techniques often take the path of least resistance and serve up obvious results. In this specific case, the scaling in daisy() rescales variables to [0,1] and this will make two-category factors (gender, subscription status, home ownership) more influential. Overall, this demonstrates why you should expect to try several methods and iterate in order to find something useful.
plot(seg.hc)
rect.hclust(seg.hc, k=7, border="red")
seg.hc.segment <- cutree(seg.hc, k=7) # membership vector for 4 groups
table(seg.hc.segment)
seg.hc.segment 1 2 3 4 5 6 7 66 58 71 65 11 22 7
seg.summ(seg.df, seg.hc.segment)
Group.1 | age | gender | income | kids | ownHome | subscribe |
---|---|---|---|---|---|---|
1 | 36.02187 | 2.000000 | 45227.51 | 1.3484848 | 1 | 1 |
2 | 46.20416 | 2.000000 | 54263.61 | 1.2758621 | 2 | 1 |
3 | 38.02229 | 1.000000 | 51550.98 | 1.4225352 | 1 | 1 |
4 | 46.41795 | 1.000000 | 56172.15 | 1.0307692 | 2 | 1 |
5 | 40.95582 | 1.000000 | 52146.57 | 1.7272727 | 2 | 2 |
6 | 35.82935 | 1.545455 | 40456.14 | 1.1363636 | 1 | 2 |
7 | 49.58583 | 2.000000 | 53385.61 | 0.8571429 | 2 | 2 |
K-means clustering attempts to find groups that are most compact, in terms of the mean sum-of-squares deviation of each observation from the multivariate center (centroid) of its assigned group.
Because it explicitly computes a mean deviation, k-means clustering relies on Euclidean distance. Thus it is only appropriate for numeric data or data that can be reasonably coerced to numeric. In our seg.df data, we have a mix of numeric and binary factors. Unlike higher-order categorical variables, binary factors can be coerced to numeric with no alteration of meaning.
Although it is not optimal to cluster binary values with k-means, given that we have a mixture of binary and numeric data, we might attempt it. Our first step is to create a variant of seg.df that is recoded to numeric. We make a copy of seg.df and use ifelse() to recode the binary factors:
seg.df.num <- seg.df
seg.df.num$gender <- ifelse(seg.df$gender=="Male", 0, 1)
seg.df.num$ownHome <- ifelse(seg.df$ownHome=="ownNo", 0, 1)
seg.df.num$subscribe <- ifelse(seg.df$subscribe=="subNo", 0, 1)
summary(seg.df.num)
age gender income kids Min. :19.26 Min. :0.0000 Min. : -5183 Min. :0.00 1st Qu.:33.01 1st Qu.:0.0000 1st Qu.: 39656 1st Qu.:0.00 Median :39.49 Median :1.0000 Median : 52014 Median :1.00 Mean :41.20 Mean :0.5233 Mean : 50937 Mean :1.27 3rd Qu.:47.90 3rd Qu.:1.0000 3rd Qu.: 61403 3rd Qu.:2.00 Max. :80.49 Max. :1.0000 Max. :114278 Max. :7.00 ownHome subscribe Min. :0.00 Min. :0.0000 1st Qu.:0.00 1st Qu.:0.0000 Median :0.00 Median :0.0000 Mean :0.47 Mean :0.1333 3rd Qu.:1.00 3rd Qu.:0.0000 Max. :1.00 Max. :1.0000
seg.df.num
age | gender | income | kids | ownHome | subscribe |
---|---|---|---|---|---|
47.31613 | 0 | 49482.81 | 2 | 0 | 0 |
31.38684 | 0 | 35546.29 | 1 | 1 | 0 |
43.20034 | 0 | 44169.19 | 0 | 1 | 0 |
37.31700 | 1 | 81041.99 | 1 | 0 | 0 |
40.95439 | 1 | 79353.01 | 3 | 1 | 0 |
43.03387 | 0 | 58143.36 | 4 | 1 | 0 |
37.55696 | 0 | 19282.23 | 3 | 0 | 0 |
28.45129 | 0 | 47245.24 | 0 | 0 | 0 |
44.20268 | 1 | 48332.52 | 1 | 0 | 0 |
35.15167 | 1 | 52567.89 | 0 | 1 | 0 |
47.61097 | 0 | 47917.56 | 0 | 1 | 0 |
37.58036 | 1 | 65767.48 | 0 | 0 | 0 |
33.21556 | 1 | 69337.66 | 2 | 0 | 0 |
43.18109 | 0 | 57200.15 | 3 | 1 | 0 |
39.35748 | 0 | 47099.77 | 1 | 1 | 0 |
42.03056 | 0 | 57036.84 | 3 | 1 | 0 |
41.96189 | 1 | 53127.06 | 0 | 1 | 0 |
43.98136 | 1 | 41254.80 | 0 | 1 | 0 |
37.61905 | 1 | 63528.47 | 1 | 1 | 0 |
32.41215 | 1 | 52649.54 | 2 | 1 | 1 |
47.94154 | 1 | 48197.81 | 1 | 1 | 0 |
36.70162 | 1 | 46163.38 | 6 | 1 | 0 |
30.47705 | 0 | 44151.93 | 2 | 0 | 0 |
38.32010 | 1 | 56128.01 | 2 | 1 | 0 |
32.72702 | 1 | 62519.89 | 4 | 1 | 0 |
40.42549 | 1 | 51195.97 | 1 | 1 | 0 |
26.86546 | 0 | 59440.87 | 2 | 0 | 0 |
44.50259 | 0 | 57362.59 | 0 | 0 | 0 |
35.16896 | 1 | 56112.12 | 1 | 0 | 0 |
39.61932 | 1 | 76488.70 | 2 | 0 | 0 |
... | ... | ... | ... | ... | ... |
32.33691 | 1 | 52776.42 | 1 | 1 | 1 |
42.03798 | 1 | 58756.32 | 2 | 0 | 0 |
38.87477 | 1 | 44811.78 | 5 | 1 | 0 |
40.16990 | 1 | 44125.40 | 4 | 1 | 0 |
37.41808 | 1 | 36257.93 | 1 | 0 | 0 |
39.14320 | 1 | 45347.01 | 1 | 1 | 1 |
29.76301 | 1 | 50720.22 | 0 | 0 | 0 |
36.23860 | 1 | 46540.88 | 1 | 0 | 1 |
37.24773 | 1 | 50560.17 | 3 | 1 | 0 |
37.35856 | 1 | 59663.28 | 1 | 0 | 0 |
31.69081 | 1 | 55940.78 | 3 | 0 | 0 |
34.03454 | 1 | 66007.99 | 3 | 0 | 0 |
36.89752 | 1 | 39366.95 | 1 | 0 | 1 |
38.16915 | 1 | 54509.25 | 2 | 0 | 0 |
42.37342 | 1 | 49308.97 | 2 | 0 | 0 |
40.30668 | 1 | 54740.28 | 0 | 1 | 0 |
36.24118 | 0 | 49775.29 | 1 | 0 | 1 |
35.82586 | 1 | 41766.29 | 2 | 0 | 1 |
26.40436 | 1 | 73240.62 | 2 | 0 | 0 |
42.40763 | 0 | 53520.36 | 1 | 0 | 0 |
35.88063 | 0 | 66423.74 | 1 | 0 | 0 |
36.22989 | 1 | 41192.28 | 4 | 0 | 0 |
39.54330 | 1 | 58933.34 | 2 | 1 | 1 |
35.79961 | 1 | 52352.69 | 1 | 0 | 1 |
36.14964 | 0 | 40522.39 | 0 | 1 | 0 |
32.95227 | 1 | 43882.43 | 0 | 1 | 0 |
40.96255 | 1 | 64197.09 | 2 | 0 | 0 |
38.22980 | 0 | 47580.93 | 0 | 0 | 1 |
33.17036 | 0 | 60747.34 | 1 | 0 | 0 |
34.38388 | 0 | 53674.93 | 5 | 1 | 0 |
We now run the kmeans() algorithm, which specifically requires specifying the number of clusters to find. We ask for four clusters with centers=4:
set.seed(96743)
seg.k <- kmeans(seg.df.num, centers=4)
We use our custom function seg.summ() to do a quick check of the data by proposed group, where cluster assignments are found in the $cluster vector inside the seg.k model:
seg.summ(seg.df, seg.k$cluster)
Group.1 | age | gender | income | kids | ownHome | subscribe |
---|---|---|---|---|---|---|
1 | 56.37245 | 1.428571 | 92287.07 | 0.4285714 | 1.857143 | 1.142857 |
2 | 29.58704 | 1.571429 | 21631.79 | 1.0634921 | 1.301587 | 1.158730 |
3 | 44.42051 | 1.452632 | 64703.76 | 1.2947368 | 1.421053 | 1.073684 |
4 | 42.08381 | 1.454545 | 48208.86 | 1.5041322 | 1.528926 | 1.165289 |
Unlike with hclust() we now see some interesting differences; the groups appear to vary by age, gender, kids, income, and home ownership. For example, we can visually check the distribution of income according to segment (which kmeans() stored in seg.k$cluster) using boxplot():
boxplot(seg.df.num$income ∼ seg.k$cluster, ylab="Income", xlab="Cluster")
The result is figure, which shows substantial differences in income by segment.
Note that in clustering models, the group labels are in arbitrary order, so don’t worry if your solution shows the same pattern with different labels.
We visualize the clusters by plotting them against a dimensional plot. clusplot() will perform dimensional reduction with principal components or multidimensional scaling as the data warrant, and then plot the observations with cluster membership identified
We use clusplot from the cluster package with arguments to color the groups, shade the ellipses for group membership, label only the groups (not the individual points) with labels=4, and omit distance lines between groups "(lines=0):
clusplot(seg.df, seg.k$cluster, color=TRUE, shade=TRUE,labels=4, lines=0, main="K-means cluster plot")
The code produces the plot in figure, which plots cluster assignment by color and ellipses against the first two principal components of the predictors (See last lecture) . Groups 3 and 4 are largely overlapping (in this dimensional reduction) while group 1 and especially group 2 are modestly differentiated.
Overall, this is a far more interesting cluster solution for our segmentation data than the hclust() proposal. The groups here are clearly differentiated on key variables such as age and income.
This may suggest a business strategy. In the present case, for instance, we see that group 1 is modestly well differentiated, and has the highest average income. That may make it a good target for a potential campaign. Many other strategies are possible, too; the key point is that the analysis provides interesting options to consider.
set.seed(96743)
seg.k <- kmeans(seg.df.num, centers=3)
seg.summ(seg.df, seg.k$cluster)
Group.1 | age | gender | income | kids | ownHome | subscribe |
---|---|---|---|---|---|---|
1 | 49.10841 | 1.360656 | 78160.35 | 0.9508197 | 1.540984 | 1.081967 |
2 | 31.29751 | 1.571429 | 23147.31 | 0.9857143 | 1.328571 | 1.157143 |
3 | 42.44649 | 1.479290 | 52620.52 | 1.5029586 | 1.502959 | 1.142012 |
boxplot(seg.df.num$income ∼ seg.k$cluster, ylab="Income", xlab="Cluster")
clusplot(seg.df, seg.k$cluster, color=TRUE, shade=TRUE,labels=3, lines=0, main="K-means cluster plot")
set.seed(96743)
seg.k <- kmeans(seg.df.num, centers=5)
seg.summ(seg.df, seg.k$cluster)
boxplot(seg.df.num$income ∼ seg.k$cluster, ylab="Income", xlab="Cluster")
clusplot(seg.df, seg.k$cluster, color=TRUE, shade=TRUE,labels=5, lines=0, main="K-means cluster plot")
Group.1 | age | gender | income | kids | ownHome | subscribe |
---|---|---|---|---|---|---|
1 | 57.01495 | 1.500000 | 98230.22 | 0.2857143 | 1.857143 | 1.000000 |
2 | 42.70781 | 1.462500 | 45234.25 | 1.3625000 | 1.550000 | 1.175000 |
3 | 46.60014 | 1.333333 | 71618.19 | 1.0980392 | 1.431373 | 1.098039 |
4 | 42.23181 | 1.505376 | 57049.21 | 1.5913978 | 1.473118 | 1.118280 |
5 | 29.69186 | 1.564516 | 21433.76 | 1.0322581 | 1.306452 | 1.161290 |
Whereas clustering is the process of discovering group membership, classification is the prediction of membership. In this section we look at example of classification: predicting segment membership.
Other example you can practyice is predicting who is likely to subscribe to a service.
As usual with classification problems, we split the data into a training sample and a test sample:
set.seed(92118)
train.prop <- 0.65
train.cases <- sample(nrow(seg.df), nrow(seg.df)*train.prop)
sub.df.train <- seg.df[train.cases, ]
sub.df.test <- seg.df[-train.cases, ]
Next, we wonder how difficult it will be to identify potential subscribers. Are subscribers in the training set well differentiated from non-subscribers? We use clusplot() to check the differentiation, removing subscribe from the data with [, -6] and using it instead as the cluster identifier:
clusplot(sub.df.train[, -6], sub.df.train$subscribe, color=TRUE, shade=TRUE, labels=4, lines=0, main="Subscriber clusters, training data")
The result in figure shows that the subscribers and non-subscribers are not well differentiated when plotted against principal components (which reflect almost 58% of the variance in the data). This suggests that the problem will be difficult!
set.seed(11954)
(sub.rf <- randomForest(subscribe ∼ ., data=sub.df.train,ntree=3000))
Call: randomForest(formula = subscribe ~ ., data = sub.df.train, ntree = 3000) Type of random forest: classification Number of trees: 3000 No. of variables tried at each split: 2 OOB estimate of error rate: 14.36% Confusion matrix: subNo subYes class.error subNo 167 3 0.01764706 subYes 25 0 1.00000000
The results are not encouraging. Although the error rate might initially sound good at 14.9 %, we have 100% error in predicting subscribers (subYes) with all 25 misclassified in the OOB data.
Why? This demonstrates the class imbalance problem in machine learning. When one category dominates the data, it is very difficult to learn to predict other groups.
set.seed(11954)
(sub.rf <- randomForest(subscribe ∼ ., data=sub.df.train, ntree=3000,sampsize=c(25, 25)) )
Call: randomForest(formula = subscribe ~ ., data = sub.df.train, ntree = 3000, sampsize = c(25, 25)) Type of random forest: classification Number of trees: 3000 No. of variables tried at each split: 2 OOB estimate of error rate: 34.36% Confusion matrix: subNo subYes class.error subNo 120 50 0.2941176 subYes 17 8 0.6800000
Although our overall error rate is substantially higher at 34.36%, we are successfully predicting 32% (i.e., 1−0.68) of the subscribers in the OOB data, which is greatly improved over zero.
We use predict() to apply the RF model to the holdout data and examine the confusion matrix:
sub.rf.sub <- predict(sub.rf, sub.df.test)
table(sub.rf.sub, sub.df.test$subscribe)
sub.rf.sub subNo subYes subNo 72 9 subYes 18 6
The model correctly predicts 6 of the 24 subscribers in the holdout data, at a cost of incorrectly predicting 9 others as subscribers who are not. That may be an acceptable tradeoff if we are trying to identify prospects who are worth an effort to reach. For instance, in the present case, calling all prospects would result in 15/105 successes (14% success rate), while calling the suggested ones would result in 6/17 successes (35 %). The ultimate value of each strategy—to call of them or not— depends on the cost of calling vs. the value of successful conversion.
Is the model predicting better than chance? We use adjustedRandIndex() to find that performance is modestly better than chance, and we confirm this with cohen.kappa() in the psych package, which provides confidence intervals:
adjustedRandIndex(sub.rf.sub, sub.df.test$subscribe)
cohen.kappa(cbind(sub.rf.sub, sub.df.test$subscribe))
Warning message in any(abs(bounds)): "coercing argument of type 'double' to logical"
Call: cohen.kappa1(x = x, w = w, n.obs = n.obs, alpha = alpha, levels = levels) Cohen Kappa and Weighted Kappa correlation coefficients and confidence boundaries lower estimate upper unweighted kappa -0.051 0.16 0.37 weighted kappa -0.051 0.16 0.37 Number of subjects = 105
With an adjusted Rand Index $= 0.10$ and Cohen’s kappa $= 0.16$ , the model identifies subscribers in the test data modestly better than chance.