The cluster Analysis is performed on the SNS dataset.Clustering with k-means can be applied by identifying groups. Clustering can automate the process of discovering the natural segments in this population. However, it will be up to us to decide whether or not the clusters are interesting and how we can use them for advertising.
SNS dataset representing a random sample of 30,000 U.S. high school students who had profiles on a well-known SNS in 2006. To protect the users’ anonymity, the SNS will remain unnamed. However, at the time the data was collected, the SNS was a popular web destination for US teenagers. Therefore, it is reasonable to assume that the profiles represent a fairly wide cross section of American adolescents in 2006.
The data was sampled evenly across four high school graduation years (2006 through 2009) representing the senior, junior, sophomore, and freshman classes at the time of data collection. Using an automated web crawler, the full text of the SNS profiles were downloaded, and each teen’s gender, age, and number of SNS friends was recorded.
A text mining tool was used to divide the remaining SNS page content into words. From the top 500 words appearing across all the pages, 36 words were chosen to represent five categories of interests: namely extracurricular activities, fashion, religion, romance, and antisocial behavior. The 36 words include terms such as football, sexy, kissed, bible, shopping, death, and drugs. The final dataset indicates, for each person, how many times each word appeared in the person’s SNS profile.
##### Chapter 9: Clustering with k-means
## Example: Finding Teen Market Segments ----
## Step 2: Exploring and preparing the data ----
teens <- read.csv("C:/Users/Bhawna/Documents/blog/data/snsdata.csv")
str(teens)## 'data.frame': 30000 obs. of 40 variables:
## $ gradyear : int 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 ...
## $ gender : Factor w/ 2 levels "F","M": 2 1 2 1 NA 1 1 2 1 1 ...
## $ age : num 19 18.8 18.3 18.9 19 ...
## $ friends : int 7 0 69 0 10 142 72 17 52 39 ...
## $ basketball : int 0 0 0 0 0 0 0 0 0 0 ...
## $ football : int 0 1 1 0 0 0 0 0 0 0 ...
## $ soccer : int 0 0 0 0 0 0 0 0 0 0 ...
## $ softball : int 0 0 0 0 0 0 0 1 0 0 ...
## $ volleyball : int 0 0 0 0 0 0 0 0 0 0 ...
## $ swimming : int 0 0 0 0 0 0 0 0 0 0 ...
## $ cheerleading: int 0 0 0 0 0 0 0 0 0 0 ...
## $ baseball : int 0 0 0 0 0 0 0 0 0 0 ...
## $ tennis : int 0 0 0 0 0 0 0 0 0 0 ...
## $ sports : int 0 0 0 0 0 0 0 0 0 0 ...
## $ cute : int 0 1 0 1 0 0 0 0 0 1 ...
## $ sex : int 0 0 0 0 1 1 0 2 0 0 ...
## $ sexy : int 0 0 0 0 0 0 0 1 0 0 ...
## $ hot : int 0 0 0 0 0 0 0 0 0 1 ...
## $ kissed : int 0 0 0 0 5 0 0 0 0 0 ...
## $ dance : int 1 0 0 0 1 0 0 0 0 0 ...
## $ band : int 0 0 2 0 1 0 1 0 0 0 ...
## $ marching : int 0 0 0 0 0 1 1 0 0 0 ...
## $ music : int 0 2 1 0 3 2 0 1 0 1 ...
## $ rock : int 0 2 0 1 0 0 0 1 0 1 ...
## $ god : int 0 1 0 0 1 0 0 0 0 6 ...
## $ church : int 0 0 0 0 0 0 0 0 0 0 ...
## $ jesus : int 0 0 0 0 0 0 0 0 0 2 ...
## $ bible : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hair : int 0 6 0 0 1 0 0 0 0 1 ...
## $ dress : int 0 4 0 0 0 1 0 0 0 0 ...
## $ blonde : int 0 0 0 0 0 0 0 0 0 0 ...
## $ mall : int 0 1 0 0 0 0 2 0 0 0 ...
## $ shopping : int 0 0 0 0 2 1 0 0 0 1 ...
## $ clothes : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hollister : int 0 0 0 0 0 0 2 0 0 0 ...
## $ abercrombie : int 0 0 0 0 0 0 0 0 0 0 ...
## $ die : int 0 0 0 0 0 0 0 0 0 0 ...
## $ death : int 0 0 1 0 0 0 0 0 0 0 ...
## $ drunk : int 0 0 0 0 1 1 0 0 0 0 ...
## $ drugs : int 0 0 0 0 1 0 0 0 0 0 ...# look at missing data for female variable
table(teens$gender)##
## F M
## 22054 5222table(teens$gender, useNA = "ifany")##
## F M <NA>
## 22054 5222 2724# look at missing data for age variable
summary(teens$age)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.086 16.310 17.290 17.990 18.260 106.900 5086# eliminate age outliers
teens$age <- ifelse(teens$age >= 13 & teens$age < 20,
teens$age, NA)
summary(teens$age)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 13.03 16.30 17.26 17.25 18.22 20.00 5523# reassign missing gender values to "unknown"
teens$female <- ifelse(teens$gender == "F" &
!is.na(teens$gender), 1, 0)
teens$no_gender <- ifelse(is.na(teens$gender), 1, 0)
# check our recoding work
table(teens$gender, useNA = "ifany")##
## F M <NA>
## 22054 5222 2724table(teens$female, useNA = "ifany")##
## 0 1
## 7946 22054table(teens$no_gender, useNA = "ifany")##
## 0 1
## 27276 2724# finding the mean age by cohort
mean(teens$age) # doesn't work## [1] NAmean(teens$age, na.rm = TRUE) # works## [1] 17.25243# age by cohort
aggregate(data = teens, age ~ gradyear, mean, na.rm = TRUE)## gradyear age
## 1 2006 18.65586
## 2 2007 17.70617
## 3 2008 16.76770
## 4 2009 15.81957# create a vector with the average age for each gradyear, repeated by person
ave_age <- ave(teens$age, teens$gradyear,
FUN = function(x) mean(x, na.rm = TRUE))
teens$age <- ifelse(is.na(teens$age), ave_age, teens$age)
# check the summary results to ensure missing values are eliminated
summary(teens$age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.03 16.28 17.24 17.24 18.21 20.00## Step 3: Training a model on the data ----
interests <- teens[5:40]
interests_z <- as.data.frame(lapply(interests, scale))
set.seed(2345)
teen_clusters <- kmeans(interests_z, 5)## Step 4: Evaluating model performance ----
# look at the size of the clusters
teen_clusters$size## [1] 871 600 5981 1034 21514# look at the cluster centers
teen_clusters$centers## basketball football soccer softball volleyball swimming
## 1 0.16001227 0.2364174 0.10385512 0.07232021 0.18897158 0.23970234
## 2 -0.09195886 0.0652625 -0.09932124 -0.01739428 -0.06219308 0.03339844
## 3 0.52755083 0.4873480 0.29778605 0.37178877 0.37986175 0.29628671
## 4 0.34081039 0.3593965 0.12722250 0.16384661 0.11032200 0.26943332
## 5 -0.16695523 -0.1641499 -0.09033520 -0.11367669 -0.11682181 -0.10595448
## cheerleading baseball tennis sports cute
## 1 0.3931445 0.02993479 0.13532387 0.10257837 0.37884271
## 2 -0.1101103 -0.11487510 0.04062204 -0.09899231 -0.03265037
## 3 0.3303485 0.35231971 0.14057808 0.32967130 0.54442929
## 4 0.1856664 0.27527088 0.10980958 0.79711920 0.47866008
## 5 -0.1136077 -0.10918483 -0.05097057 -0.13135334 -0.18878627
## sex sexy hot kissed dance band
## 1 0.020042068 0.11740551 0.41389104 0.06787768 0.22780899 -0.10257102
## 2 -0.042486141 -0.04329091 -0.03812345 -0.04554933 0.04573186 4.06726666
## 3 0.002913623 0.24040196 0.38551819 -0.03356121 0.45662534 -0.02120728
## 4 2.028471066 0.51266080 0.31708549 2.97973077 0.45535061 0.38053621
## 5 -0.097928345 -0.09501817 -0.13810894 -0.13535855 -0.15932739 -0.12167214
## marching music rock god church jesus
## 1 -0.10942590 0.1378306 0.05905951 0.03651755 -0.00709374 0.01458533
## 2 5.25757242 0.4981238 0.15963917 0.09283620 0.06414651 0.04801941
## 3 -0.10880541 0.2844999 0.21436936 0.35014919 0.53739806 0.27843424
## 4 -0.02014608 1.1367885 1.21013948 0.41679142 0.16627797 0.12988313
## 5 -0.11098063 -0.1532006 -0.12460034 -0.12144246 -0.15889274 -0.08557822
## bible hair dress blonde mall shopping
## 1 -0.03692278 0.43807926 0.14905267 0.06137340 0.60368108 0.79806891
## 2 0.05863810 -0.04484083 0.07201611 -0.01146396 -0.08724304 -0.03865318
## 3 0.22990963 0.23612853 0.39407628 0.03471458 0.48318495 0.66327838
## 4 0.08478769 2.55623737 0.53852195 0.36134138 0.62256686 0.27101815
## 5 -0.06813159 -0.20498730 -0.14348036 -0.02918252 -0.18625656 -0.22865236
## clothes hollister abercrombie die death
## 1 0.5651537331 4.1521844 3.96493810 0.043475966 0.09857501
## 2 -0.0003526292 -0.1678300 -0.14129577 0.009447317 0.05135888
## 3 0.3759725120 -0.0553846 -0.07417839 0.037989066 0.11972190
## 4 1.2306917174 0.1610784 0.26324494 1.712181870 0.93631312
## 5 -0.1865419798 -0.1557662 -0.14861104 -0.094875180 -0.08370729
## drunk drugs
## 1 0.035614771 0.03443294
## 2 -0.086773220 -0.06878491
## 3 -0.009688746 -0.05973769
## 4 1.897388200 2.73326605
## 5 -0.087520105 -0.11423381## Step 5: Improving model performance ----
# apply the cluster IDs to the original data frame
teens$cluster <- teen_clusters$cluster
# look at the first five records
teens[1:5, c("cluster", "gender", "age", "friends")]## cluster gender age friends
## 1 5 M 18.982 7
## 2 3 F 18.801 0
## 3 5 M 18.335 69
## 4 5 F 18.875 0
## 5 4 <NA> 18.995 10# mean age by cluster
aggregate(data = teens, age ~ cluster, mean)## cluster age
## 1 1 16.86497
## 2 2 17.39037
## 3 3 17.07656
## 4 4 17.11957
## 5 5 17.29849# proportion of females by cluster
aggregate(data = teens, female ~ cluster, mean)## cluster female
## 1 1 0.8381171
## 2 2 0.7250000
## 3 3 0.8378198
## 4 4 0.8027079
## 5 5 0.6994515# mean number of friends by cluster
aggregate(data = teens, friends ~ cluster, mean)## cluster friends
## 1 1 41.43054
## 2 2 32.57333
## 3 3 37.16185
## 4 4 30.50290
## 5 5 27.70052Reference: Machine Learning with R - Second Edition - Jul 31, 2015 by Brett Lantz
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