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2016_SISBID_Clustering_Lab.R

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+#############################################################
+#2016 SISBID Module 5 - Unsupervised Learning
+#Genevera I. Allen & Yufeng Liu
+#Clustering Lab
+############################################################
+
+############
+#Data set - Author Data. This data set consists of word counts from chapters written by four British authors.
+#This lab will put together concepts from both dimension reduction and clustering.
+#There are ultimately 3 goals to this lab:
+#1) Correctly cluster author texts in an unsupervised manner.
+#2) Determine which words are responsible for correctly separating the author texts.
+#3) Visualize the author texts, words and the results of your analysis
+#############
+
+
+#############
+#Problem 1 - Visualization
+#############
+#Problem 1a - We wish to plot the author texts as well as the words via a 2D scatterplot.  Which method would be best to use?  Why?
+
+#Problem 1b - Apply PCA to visualize the author texts.  Explain the results.
+
+#Problem 1c - Apply MDS to visualize the author texts.  Interpret the results.
+
+#Problem 1d - Can you use MDS to help determine which distance is appropriate for this data?  Which one is best and why?
+
+#Problem 1e - Apply MDS with your chosen distance to visualize the words.  Interpret the results.
+
+##########
+#Problem 2 - K-means
+##########
+#Problem 2a - Apply K-means with K=4 to this data.
+
+#Problem 2b - How well does K-mean do at separating the authors?
+
+#Problem 2c - Is K-means an appropriate clustering algorithm for this data?  Why or Why nor?
+
+#############
+#Problem 3 - Hierarchical Clustering
+#############
+#Problem 3a - Apply hierarchical clustering to this data set.
+
+#Problem 3b - Which distance is best to use?  Why?
+
+#Problem 3c - Which linkage is best to use?  Why?
+
+#Problem 3d - Do any linkages perform particularly poorly?  Explain this result.
+
+#Problem 3e - Visualize your hierarchical clustering results.
+
+###########
+#Problem 4 - Biclustering
+###########
+
+#Problem 4a - Apply the cluster heatmap method to visualize this data.  Which distance and linkage functions did you use?
+
+#Problem 4b - Interpret the cluster heatmap.  Which words are important for distinguishing author texts?
+
+
+###########
+#Problem 5 - NMF
+###########
+
+#Problem 5a - Apply NMF with K = 4 and use W to assign cluster labels to each observation.
+
+#Problem 5b - How well does NMF perform?  Interpret and explain this result.
+
+#Problem 5c - Can you use the NMF to determine which words are important for distinguishing author texts?  How?  What did you find?
+
+#############
+#Problem 6 - Wrap-up
+############
+#Problem  6a - Overall, which method is the best at clustering the author texts?  Why is this the case?
+
+#Problem 6b - Which words are key for distinguishing the author texts?  How did you determine these?
+
+#Problem 6c - Overall, which is the best method for providing a visual summary of the data?
+
+#######################
+
+
+###############################################################
+###############################################################
+#R scripts to help out with the Clustering Lab
+#Don't peek at this if you want to practice coding on your own!!
+##################################################################
+
+
+#######################
+#author data
+
+load("UnsupL_SISBID_2016.RData")
+
+#understand the data a bit
+dim(author)
+colnames(author)
+unique(rownames(author))
+TrueAuth = as.factor(rownames(author))
+
+par(mfrow=c(2,2)) 
+hist(author[,colnames(author)=="the"],breaks=25)
+hist(author[,colnames(author)=="a"],breaks=25)
+hist(author[,colnames(author)=="and"],breaks=25)
+hist(author[,colnames(author)=="things"],breaks=25)
+
+#take out bookID 
+X = author[,1:69] 
+
+
+#############
+#Visulaizing data - how to visulaize texts?  words? in 2-dimensions
+
+#trying PCA
+sv = svd(X);
+V = sv$v
+Z = X%*%V;
+plot(Z[,1],Z[,2],type="n")
+text(Z[,1],Z[,2],rownames(X),col=as.numeric(TrueAuth),cex=.5)
+#why doesn't this work well?
+
+########
+#trying MDS (classical)
+#can you use MDS to decide which distance is best to understand this data? 
+
+#visualizing author texts
+Dmat = dist(X,method="canberra")
+mdsres = cmdscale(Dmat,k=2)
+plot(mdsres[,1],mdsres[,2],type="n")
+text(mdsres[,1],mdsres[,2],rownames(X),col=as.numeric(TrueAuth),cex=.5)
+
+#visulaizing words
+Dmat = dist(t(X),method="canberra")
+mdsresW = cmdscale(Dmat,k=2)
+plot(mdsresW[,1],mdsresW[,2],type="n")
+text(mdsresW[,1],mdsresW[,2],colnames(X))
+
+##############
+#K- means
+K = 4
+km = kmeans(X,centers=K)
+table(km$cluster,TrueAuth)
+
+plot(mdsres[,1],mdsres[,2],type="n")
+text(mdsres[,1],mdsres[,2],rownames(X),col=km$cluster,cex=.5)
+
+###############
+#hierarchical clustering
+#which distance is appropraite?  Why?  
+
+Dmat = dist(X,method="canberra")
+com.hc = hclust(Dmat,method="complete")
+res.com = cutree(com.hc,4)
+table(res.com,TrueAuth)
+
+plot(com.hc,cex=.5)
+
+#which linkage is best?  Why?
+
+Dmat = dist(X,method="canberra")
+com.hc = hclust(Dmat,method="ward.D")
+res.com = cutree(com.hc,4)
+table(res.com,TrueAuth)
+
+plot(com.hc,cex=.5)
+
+#do any preform terribly?  Why?
+
+#visualize hierarchical clustering reuslts using MDS
+table(res.com,TrueAuth)
+plot(mdsres[,1],mdsres[,2],type="n")
+text(mdsres[,1],mdsres[,2],rownames(X),col=res.com,cex=.5)
+
+
+#############
+#cluster heatmap
+
+heatmap(X,distfun=function(x)dist(x,method="canberra"),hclustfun=function(x)hclust(x,method="ward.D"))
+
+heatmap(scale(X),distfun=function(x)dist(x,method="canberra"),hclustfun=function(x)hclust(x,method="ward.D"))
+
+#############
+#NMF
+library("NMF")
+K = 4
+nmffit = nmf(X,rank=K) 
+W = basis(nmffit)
+H = coef(nmffit)
+
+cmap = apply(W,1,which.max)
+table(cmap,TrueAuth)
+
+par(mfrow=c(1,2))
+basismap(nmffit,annRow=rownames(X),scale="col",legend=FALSE)
+coefmap(nmffit,annCol=colnames(X),scale="col",legend=FALSE)
+
+par(mfrow=c(1,1))
+coefmap(nmffit,annCol=colnames(X),scale="col",legend=FALSE)
+
+#which words are most important for distinguishing authors?
+
+########################################################