Mining Massive Datasets这周的课讲聚类和计算广告学：二分图匹配。课后的作业好几个都是一眼看不出来，只好写程序算。
其中有一道题是这个样子：
We wish to cluster the following set of points:
into 10 clusters. We initially choose each of the green points(25,125), (44,105), (29,97), (35,63), (55,63), (42,57), (23,40),(64,37), (33,22), and (55,20) as a centroid. Assign each of thegold points to their nearest centroid. (Note: the scales of thehorizontal and vertical axes differ, so you really need to applythe formula for distance of points; you can’t just “eyeball” it.)Then, recompute the centroids of each of the clusters. Do any ofthe points then get reassigned to a new cluster on the next round?Identify the true statement in the list below. Each statementrefers either to a centroid AFTER recomputation of centroids(precise to one decimal place) or to a point that getsreclassified.
第一个念头就是用R捯饬：
points <- matrix(c(28, 65, 50, 25, 55, 38, 44, 29, 50, 63, 43, 35, 55, 50, 42,
23, 64, 50, 33, 55, 145, 140, 130, 125, 118, 115, 105, 97, 90, 88, 83, 63,
63, 60, 57, 40, 37, 30, 22, 20), ncol = 2)
colnames(points) <- c("x", "y")
centroids <- matrix(c(25, 44, 29, 35, 55, 42, 23, 64, 33, 55, 125, 105, 97,
63, 63, 57, 40, 37, 22, 20), ncol = 2)
dimnames(centroids)[[2]] <- c("x", "y")
cl <- kmeans(points, centroids, trace = TRUE)
## KMNS(*, k=10): iter= 1, indx=10
## QTRAN(): istep=20, icoun=13
## QTRAN(): istep=40, icoun=14
## KMNS(*, k=10): iter= 2, indx=20
plot(points, col = cl$cluster)
points(cl$centers, col = 1:2, pch = 8, cex = 2)
cl
## K-means clustering with 10 clusters of sizes 3, 3, 5, 1, 2, 1, 1, 1, 1, 2
##
## Cluster means:
## x y
## 1 30.33 128.3
## 2 56.67 129.3
## 3 45.80 92.6
## 4 35.00 63.0
## 5 52.50 61.5
## 6 42.00 57.0
## 7 23.00 40.0
## 8 64.00 37.0
## 9 33.00 22.0
## 10 52.50 25.0
##
## Clustering vector:
## [1] 1 2 2 1 2 1 3 3 3 3 3 4 5 5 6 7 8 10 9 10
##
## Within cluster sum of squares by cluster:
## [1] 559.3 359.3 900.0 0.0 17.0 0.0 0.0 0.0 0.0 62.5
## (between_SS / total_SS = 94.4 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
运行结果和问题的四个选择项都配不上。kmeans做了两轮计算，返回的是最终结果，而第一轮结果即使加了trace参数也弄不出来。最后只好默默抄起了Java，很受伤:(