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")

[1] "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.

kosDist = dist(dailykos, method="euclidean")
kosHierClust = hclust(kosDist, method="ward.D")

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

• We have a lot of observations, so it takes a long time to compute the distance between each pair of observations.

• We have a lot of variables, so the distance computation is long.

Plot the dendrogram of your hierarchical clustering model.

plot(kosHierClust)

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.

hierGroups = cutree(kosHierClust, k=7)
HierCluster1 = subset(dailykos, hierGroups == 1)
HierCluster2 = subset(dailykos, hierGroups == 2)
HierCluster3 = subset(dailykos, hierGroups == 3)
HierCluster4 = subset(dailykos, hierGroups == 4)
HierCluster5 = subset(dailykos, hierGroups == 5)
HierCluster6 = subset(dailykos, hierGroups == 6)
HierCluster7 = subset(dailykos, hierGroups == 7)
table(hierGroups)

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

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?

table(hierGroups)

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

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:

tail(sort(colMeans(HierCluster1)))

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

• bush

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.

tail(sort(colMeans(HierCluster2)))

     bush  democrat challenge      vote      poll  november 
    2.847     2.850     4.097     4.399     4.847    10.340 

tail(sort(colMeans(HierCluster3)))

     elect    parties      state republican   democrat       bush 
     1.647      1.666      2.321      2.524      3.824      4.406 

tail(sort(colMeans(HierCluster4)))

campaign    voter presided     poll     bush    kerry 
   1.432    1.540    1.626    3.590    7.835    8.439 

tail(sort(colMeans(HierCluster5)))

      american       presided administration            war           iraq           bush 
         1.091          1.120          1.231          1.776          2.428          3.941 

tail(sort(colMeans(HierCluster6)))

    race     bush    kerry    elect democrat     poll 
  0.4580   0.4888   0.5168   0.5350   0.5644   0.5812 

tail(sort(colMeans(HierCluster7)))

democrat    clark   edward     poll    kerry     dean 
   2.148    2.498    2.608    2.766    3.952    5.804 

Which words best describe cluster 2?

• november, poll, vote, challenge

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.

table(KmeansCluster$cluster)

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

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.

tail(sort(colMeans(KmeansCluster1)))

         state           iraq          kerry administration       presided           bush 
         1.610          1.616          1.637          2.664          2.767         11.432 

tail(sort(colMeans(KmeansCluster2)))

primaries  democrat    edward     clark     kerry      dean 
    2.319     2.694     2.799     3.090     4.979     8.278 

tail(sort(colMeans(KmeansCluster3)))

administration          iraqi       american           bush            war           iraq 
         1.390          1.610          1.686          2.610          3.025          4.094 

tail(sort(colMeans(KmeansCluster4)))

     elect republican      kerry       poll   democrat       bush 
    0.6011     0.6175     0.6495     0.7475     0.7891     1.1474 

tail(sort(colMeans(KmeansCluster5)))

      race     senate      state    parties republican   democrat 
     2.485      2.650      3.521      3.620      4.638      6.994 

tail(sort(colMeans(KmeansCluster6)))

 democrat      bush challenge      vote      poll  november 
    2.900     2.960     4.122     4.447     4.872    10.371 

tail(sort(colMeans(KmeansCluster7)))

presided    voter campaign     poll     bush    kerry 
   1.325    1.334    1.383    2.789    5.971    6.481 

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(hierGroups, KmeansCluster$cluster)

          
hierGroups    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？

• 將某文字資料進行分詞後找出文章中常見的單字，並畫出在所有文件中出現名詞。出現次數越多，其字體就會呈現越大。
• 格式為字詞及出現頻率之組合

使用字頻表作集群分析時，區隔變數是什麼？

• 不同的字詞及其出現頻率

從樹狀圖判斷群數和從應用需求決定群數有什麼差別？

• 根據樹狀圖，選擇那些截止點中樹狀圖中的水平線之間存在大量空間的群數比較好，但從應用需求來看有時可能群集過少則會造成類過於廣泛而無法使用

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