1. RazorFish Data Exploration-KMeans
Data Exploration utilizing the K-Means Clustering algorithm
Performed By: Hilbert G Locklear

2. K-Means
The k-means algorithm of Hartigan and Wong (1979) is used by default.
This is an improvement of the of the algorithm given by MacQueen (1967).
k-means aims to partition the points into k groups such that the sum of squares from points to the assigned cluster centers is minimized.
At the minimum, all cluster centers are at th mean of the set of data points which are nearest to the cluster center. (Voronoi set)
Multiple random restarts are used to ensure a stable clustering is produced...if one exists.
k = 1 is allowed which returns the center of the data set and wss.

3. K-Means Function Part of the stats package.
Performs k-means clustering on a data matrix.
k-means function usage
kmeans(x, centers, iter.max = 10, nstart = 1, algorithm = “Hartigan-Wong”, trace = FALSE)
x... is a numeric data matrix
centers...either the number of cluster or a set of distinct cluster centers. If a random set of rows in x is chosen as the initial centers.
iter.max...the maximum number of iterations allowed.
nstart...if centers is a number, the number of random sets to be used.
algorithm...the implementation to be used.
Hartigan-Wong
Llyod
Forgy
MacQueen
trace...if true, tracing information on the progress of the algorithm is produced.
k-means returns an object of the class kmeans which has a print and a fitted method.
fitted(object, method = c(“centers”,”classes), ...)

4. K-Means Function k-modes can return the following values:
cluster...a vector of integers (1:k) which indicates which cluster a point is assigned to.
centers...a matrix of cluster centers.
totss...the total sum of squares.
withinss...a vector of within-cluster sum of squares...each element is the wss for a cluster.
tot.withinss...total within-cluster sum of squares.
betweenss...the between-cluster sum of squares.
size...the number of points in each cluster.
iter...the number of (outer) iterations.

5. Data Kmeans was performed on both the training and testing data sets.
BOWTrainVectorized.txt and BOWTestVectorized.txt
12,500 objects each
Feature vector consist of 2 categorical variables and 7 numeric variables
Reviewer ID...Identifies the reviewer...may not be unique
Sentiment Value...Binary value (1) = positive and (0) = negative.
Total Word Count...Number of all word in the review text.
Stopword Count...Number of words in the review text that are stopwords.
Useful Word Count...Total Word Count – Stopword Count.
Good Adjective Count...Number of words in the review text that are positive adjectives.
Bad Adjective Count...Number of words in the review text that are negative adjectives.
Good Phrase Count...Number of words in the review text that are sequential, multiple word strings which represent positive sentiment.
Bad Phrase Count...Number of words in the review text that are sequential multiple word strings which represent negative sentiment.
Example Vector
Feature Vector
R_ID
S_value
Twrd_count
Swrd_count
Uwrd_count
Good_Adj
Bad_Adj
Good_Phr
Bad_Phr
0001_1
256 20
236 10 2 1

6. Procedure-R script #install required packages
install.packages(“stats")
library(stats)
#read the data into a data frame
Train_Data<-read.delim(“~BOWTrainVectorized.txt”, header = TRUE, sep =“\t”)
#perform kmeans clustering
TrainDataCluster<-kmeans(Train_Data[2:9], 3, iter.max=3, nstart=1,algorithm="Hartigan-Wong“, trace=FALSE)
TrainDataCluster

7. Results Kmeans Cluster Distribution k = 3 Cluster 1 8,762 reviews
Mixed sentiment
Mean Word Count: 145
Stop Word Count: 72
Useable Word Count: 73
Cluster 2
2,903 reviews
Negative sentiment
Mean Word Count: 357
Stop Word Count: 176
Useable Word Count: 181
Cluster 3
835 reviews
Positive sentiment
Mean Word Count: 726
Stop Word Count: 359
Useable Word Count: 367
Kmeans Cluster Distribution k = 3

8. Analysis Distinct clusters. Cluster have good cohesion.
High positive sentiment implies high mean word count.