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Cluster analysis in R by Aman Chauhan
- 2. What is Clustering? Clustering is a technique of data segmentation that partitions the data into several groups based on their similarity. We group the data through a statistical operation. These smaller groups that are formed from the bigger data are known as clusters.
- 3. What is cluster Analysis? A method of summarizing data, similar to factor analysis cases are grouped into “clusters” with other similar cases It’s a way of “grouping” data into meaningful groups or clusters
- 4. Types of Cluster analysis There are two types of cluster analysis: R and Q type analysis: R Type - to what extent do the variables covary across the cases? Q Type - to what extent do the cases covary across the variables?
- 5. Where it is used? It used in cases where the underlying input data has a colossal volume and we are tasked with finding similar subsets that can be analysed in several ways. For example – A marketing company can categorise their customers based on their economic background, age and several other factors to sell their products, in a better way.
- 6. K-Means clustering in R One of the most popular partitioning algorithms in clustering is the K-means cluster analysis in R. It is an unsupervised learning algorithm. It tries to cluster data based on their similarity. Also, we have specified the number of clusters and we want that the data must be grouped into the same clusters. The algorithm assigns each observation to a cluster and also finds the centroid of each cluster.
- 7. Code for K means Clustering in R mydata <- mtcars[, c('mpg', 'cyl', 'wt')] clusters <- kmeans(mydata, 3) kmeanPlot <- par(mar = c(5.1, 4.1, 0, 1)) plot(mydata, col = clusters$cluster)
- 8. Points to keep in mind • k-means clustering is a flat clustering technique, which produces only one partition with k clusters • requires a user to determine the number of clusters at the beginning • k-means clustering is much faster than hierarchical clustering
- 9. #Using the mtcars dataset #clean/normalize the data data(mtcars) mydata = na.omit(mtcars) #deletion of missing mydata = scale(mydata) #standarize variables # Determine number of clusters wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var)) for (i in 2:15) wss[i] <- sum(kmeans(mydata, centers=i)$withinss) plot(1:15, wss, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares") # check out the plot # K-Means Cluster Analysis fit <- kmeans(mydata, 5) # 5 cluster solution # get cluster means aggregate(mydata,by=list(fit$cluster),FUN=mean) # append cluster assignment mydata <- data.frame(mydata, fit$cluster) #visualize the clustering results library(cluster) clusplot(mydata, fit$cluster, color=TRUE, shade=TRUE, labels=2, lines=0