AS6-1 Daily Kos文章分群

主要議題：依字頻表對文章分群

學習重點：

• 依字頻表對文章分群
• 層級式集群分析：Hierarchical Cluster Analysis
• 依據樹狀圖決定要分多少群
• 依據應用決定要分多少群
• K-Means集群分析：K-Means Cluster Analysis
• 從常見字辭推論文集的主題

rm(list=ls(all=T))
Sys.setlocale("LC_ALL","C")
options(digits=4, scipen=12)
library(dplyr)

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1. Hierarchical Clustering

1.1 字頻表、距離矩陣、階層式集群分析

Let’s start by building a hierarchical clustering model. First, read the data set into R. Then, compute the distances (using method=“euclidean”), and use hclust to build the model (using method=“ward.D”). You should cluster on all of the variables.

D = read.csv('data/dailykos.csv')
dim(D)

[1] 3430 1545

D[1:20, 1:10]

   abandon abc ability abortion absolute abstain abu abuse accept access
1        0   0       0        0        0       0   0     0      0      0
2        0   0       0        0        0       0   0     0      0      0
3        0   0       0        0        0       1   0     0      0      0
4        0   0       0        0        0       0   0     0      0      0
5        0   0       0        0        0       0   0     0      0      0
6        0   0       0        0        0       0   0     0      0      0
7        0   0       0        0        0       0   0     0      0      0
8        0   0       0        0        0       0   0     0      0      0
9        0   0       0        0        0       0   0     0      0      0
10       0   0       0        0        0       0   0     0      0      0
11       0   0       0        0        0       0   0     0      0      0
12       0   0       0        0        0       1   0     0      0      0
13       0   0       0        0        0       0   0     0      0      0
14       0   0       0        0        0       0   0     0      0      0
15       0   0       0        0        0       0   0     0      0      0
16       0   0       0        0        0       0   0     0      0      0
17       0   0       0        0        0       0   0     0      0      0
18       0   0       0        0        0       0   0     0      0      0
19       0   0       0        0        0       0   0     0      0      0
20       0   0       0        0        0       0   0     0      0      0

# 距離矩陣: Distance Matrix
t0 = Sys.time()
d = dist(D, method="euclidean")
Sys.time() - t0

Time difference of 2.318 mins

Running the dist function will probably take you a while. Why? Select all that apply.

# 階層式集群分析: Hierarchical Clustering Analysis
t0 = Sys.time()
hc = hclust(d, method='ward.D')
Sys.time() - t0

Time difference of 0.6556 secs

Plot the dendrogram of your hierarchical clustering model.

plot(hc)

1.2 從樹狀圖判斷群數

Just looking at the dendrogram,

which of the following seem like good choices for the number of clusters? Select all that apply.

• 2
• 3

1.3 從應用決定群數

In this problem, we are trying to cluster news articles or blog posts into groups. This can be used to show readers categories to choose from when trying to decide what to read. Just thinking about this application,

what are good choices for the number of clusters? Select all that apply.

• 7
• 8

1.4 依群組分割資料

Let’s pick 7 clusters. This number is reasonable according to the dendrogram, and also seems reasonable for the application. Use the cutree function to split your data into 7 clusters.

kg = cutree(hc, k=7)
L = split(D, kg)

Now, we don’t really want to run tapply on every single variable when we have over 1,000 different variables. Let’s instead use the subset function to subset our data by cluster. Create 7 new datasets, each containing the observations from one of the clusters.

How many observations are in cluster 3?

nrow(L[[3]])

[1] 374

table(kg) %>% sort

kg
   4    7    2    3    5    6    1 
 139  209  321  374  407  714 1266 

Which cluster has the most observations?

• 1

Which cluster has the fewest observations?

• 4

1.5 找出第一族群中最常見的字辭

Instead of looking at the average value in each variable individually, we’ll just look at the top 6 words in each cluster. To do this for cluster 1, type the following in your R console (where “HierCluster1” should be replaced with the name of your first cluster subset):

tail(sort(colMeans(HierCluster1)))

This computes the mean frequency values of each of the words in cluster 1, and then outputs the 6 words that occur the most frequently. The colMeans function computes the column (word) means, the sort function orders the words in increasing order of the mean values, and the tail function outputs the last 6 words listed, which are the ones with the largest column means.

What is the most frequent word in this cluster, in terms of average value? Enter the word exactly how you see it in the output:

L[[1]] %>% colMeans %>% sort %>% tail

     state republican       poll   democrat      kerry       bush 
    0.7575     0.7591     0.9036     0.9194     1.0624     1.7054 

1.6 找出各族群中最常見的字辭

Now repeat the command given in the previous problem for each of the other clusters, and answer the following questions.

sapply(L, function(x) x %>% colMeans %>% sort %>% tail %>% names) %>% t

  [,1]       [,2]         [,3]             [,4]         [,5]       [,6]      
1 "state"    "republican" "poll"           "democrat"   "kerry"    "bush"    
2 "bush"     "democrat"   "challenge"      "vote"       "poll"     "november"
3 "elect"    "parties"    "state"          "republican" "democrat" "bush"    
4 "campaign" "voter"      "presided"       "poll"       "bush"     "kerry"   
5 "american" "presided"   "administration" "war"        "iraq"     "bush"    
6 "race"     "bush"       "kerry"          "elect"      "democrat" "poll"    
7 "democrat" "clark"      "edward"         "poll"       "kerry"    "dean"    

Which words best describe cluster 2?

• november, poll, vote

Which cluster could best be described as the cluster related to the Iraq war?

• 5

In 2004, one of the candidates for the Democratic nomination for the President of the United States was Howard Dean, John Kerry was the candidate who won the democratic nomination, and John Edwards with the running mate of John Kerry (the Vice President nominee). Given this information,

which cluster best corresponds to the democratic party?

• 7

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2 K-Means Clustering

2.1 K-Means集群分析

Now, run k-means clustering, setting the seed to 1000 right before you run the kmeans function. Again, pick the number of clusters equal to 7. You don’t need to add the iters.max argument.

set.seed(1000)
km = kmeans(D, 7)
kg2 = km$cluster
table(km$cluster) %>% sort

   2    1    5    3    7    6    4 
 144  146  163  277  308  329 2063 

Subset your data into the 7 clusters (7 new datasets) by using the “cluster” variable of your kmeans output.

How many observations are in Cluster 3?

• 277

Which cluster has the most observations?

• 4

Which cluster has the fewest number of observations?

• 2

2.2 找出各族群中最常見的字辭

Now, output the six most frequent words in each cluster, like we did in the previous problem, for each of the k-means clusters.

split(D, kg2) %>% sapply(function(x) 
  x %>% colMeans %>% sort %>% tail %>% names) %>% t

  [,1]             [,2]         [,3]        [,4]             [,5]         [,6]      
1 "state"          "iraq"       "kerry"     "administration" "presided"   "bush"    
2 "primaries"      "democrat"   "edward"    "clark"          "kerry"      "dean"    
3 "administration" "iraqi"      "american"  "bush"           "war"        "iraq"    
4 "elect"          "republican" "kerry"     "poll"           "democrat"   "bush"    
5 "race"           "senate"     "state"     "parties"        "republican" "democrat"
6 "democrat"       "bush"       "challenge" "vote"           "poll"       "november"
7 "presided"       "voter"      "campaign"  "poll"           "bush"       "kerry"   

Which k-means cluster best corresponds to the Iraq War?

• 3

Which k-means cluster best corresponds to the democratic party? (Remember that we are looking for the names of the key democratic party leaders.)

• 2

2.3 ~ 2.6 兩種分群結果之間的對應關係

For the rest of this problem, we’ll ask you to compare how observations were assigned to clusters in the two different methods. Use the table function to compare the cluster assignment of hierarchical clustering to the cluster assignment of k-means clustering.

table(Hierarchical=kg, KMeans=kg2)

            KMeans
Hierarchical    1    2    3    4    5    6    7
           1    3   11   64 1045   32    0  111
           2    0    0    0    0    0  320    1
           3   85   10   42   79  126    8   24
           4   10    5    0    0    1    0  123
           5   48    0  171  145    3    1   39
           6    0    2    0  712    0    0    0
           7    0  116    0   82    1    0   10

Which Hierarchical Cluster best corresponds to K-Means Cluster 2?

• 7

Which Hierarchical Cluster best corresponds to K-Means Cluster 3?

• 5

Which Hierarchical Cluster best corresponds to K-Means Cluster 7?

• No Hierarchical Cluster contains at least half of the points in K-Means Cluster 7.

Which Hierarchical Cluster best corresponds to K-Means Cluster 6?

• 2

【討論問題】

字頻表是什麼？它的資料格式？

使用字頻表作集群分析時，區隔變數是什麼？

從樹狀圖判斷群數和從應用需求決定群數有什麼差別？

統計上最好的分群也是實務上最好的分群嗎？

除了考慮群間和群間距離之外，實務上的分群通常還需要考慮那些因數？

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