STAT Statistical Learning. Clustering. Unsupervised. Learning. Clustering. April 3, 2018
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1 STAT 8 - STAT 8 - April, 8
2 STAT 8 -
3 Supervised vs. STAT 8 -
4 STAT 8 - Supervised
5 - How many clusters? STAT 8 -
6 STAT 8 -
7 STAT 8 - k-means clustering
8 k-means clustering STAT 8 - ## K-means clustering with clusters of sizes, 6, ## ## Cluster means: ## [,] [,] ## ## ## ## ## vector: ## [] ## [6] ## [7] ## ## Within cluster sum of squares by cluster: ## [] ## (between_ss / total_ss = 75. %) ## ## Available components: ## ## [] "cluster" "centers" "totss" "withinss" ## [5] "tot.withinss" "betweenss" "size" "iter" ## [9] "ifault"
9 k-means clustering - code STAT 8 - km <- kmeans(combined, ) plot(combined,type='n',axes=f, xlab='',ylab='') box() points(combined,pch=as.character(km$cluster), col=c(rep('dodgerblue',5), rep('forestgreen',5), rep('firebrick',5))) draw.circle(.,-.,.5, border='dodgerblue') draw.circle(.79,.65,., border='firebrick') draw.circle(.,.5,., border='forestgreen')
10 Hierarchical clustering STAT 8 - Cluster Dendrogram Height dist(combined) hclust (*, "complete")
11 STAT 8 - Hierarchical clustering - with clusters
12 STAT 8 - Hierarchical clustering - with clusters
13 Hierarchical clustering - code STAT 8 - hc <- hclust(dist(combined)) plot(hc, hang=-) plot(combined,type='n',axes=f, xlab='',ylab='') box() points(combined,pch=as.character(cutree(hc,)), col=c(rep('dodgerblue',5), rep('forestgreen',5), rep('firebrick',5)))
14 How to choose the number of clusters? STAT 8 - Given these plots that we have seen, how do we choose the appropriate number of clusters?
15 How to choose the number of clusters? - Scree plot STAT 8 - Within groups sum of squares Number of Clusters
16 Scree plot - code STAT 8 - wss <- rep(,5) for (i in :5) { wss[i] <- sum(kmeans(combined,centers=i)$withinss) } plot(:5, wss, type="b", xlab="number of Clusters", ylab="within groups sum of squares")
17 Data with more than dimensions STAT 8 - warm-blooded can fly vertebrate endangered have hair ant No No No No No bee No Yes No No Yes cat Yes No Yes No Yes cow Yes No Yes No Yes duc Yes Yes Yes No No eag Yes Yes Yes Yes No ele Yes No Yes Yes No fly No Yes No No No fro No No Yes Yes No lio Yes No Yes Yes Yes liz No No Yes No No lob No No No No No rab Yes No Yes No Yes spi No No No No Yes wha Yes No Yes Yes No
18 Multidimensional Scaling STAT 8 - rab cow cat spi lio bee lob fly ant liz duc ele fro whaeag
19 MDS - Code STAT 8 - animals <- cluster::animals colnames(animals) <- c("warm-blooded", "can fly", "vertebrate", "endangered", "live in groups", "have hair") animals.cluster <- animals[,-(5)] animals.cluster <- animals.cluster[-c(,5,,6,8),] animals.cluster[,] <- animals.cluster[,] <- d <- dist(animals.cluster) fit <- cmdscale(d, k=) fit.jitter <- fit + runif(nrow(fit*),-.5,.5) plot(fit.jitter[,], fit.jitter[,], xlab="", ylab="", main="", type box()
20 Hierarchical of Animals STAT 8 - Cluster Dendrogram fly ant lob bee spi liz fro ele wha lio rab cat cow duc eag Height dist(animals.cluster) hclust (*, "complete")
21 Lecture Exercise: Zoo Animals STAT 8 - Use the dataset create below for the following questions. zoo.data <- read.csv(' rownames(zoo.data) <- zoo.data[,] zoo.data <- zoo.data[,-] Use multidimensional scaling to visualize the data in two dimensions. What are two animals that are very similar and two that are very different? Create a hierachical clustering object for this dataset. Why are a leopard and raccoon clustered together for any cluster size? Now add colors corresponding to four different clusters to your MDS plot. Interpret what each of the four clusters correspond to.
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