Clustering.pptx
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Clustering is an unsupervised learning technique that partitions data into groups called clusters based on similarities between data points. There are two main types of clustering - hard clustering which assigns each data point to one cluster, and soft clustering which assigns probabilities of cluster membership. Hierarchical clustering creates hierarchical clusters where clusters have a nested tree structure, and can be visualized using a dendrogram. K-means clustering is an iterative algorithm that partitions data into a predefined number of clusters K by minimizing distances between data points and cluster centroids.
![Find The Distance
• m<-matrix(1:16, nrow=4)
• m
• dist(m,method="euclidean")
• dist(m,method="manhattan")
• dist(m,method="maximum")
• dist(m,method="canberra")
• dist(m,method="minkowski")
• x<-mtcars["Honda Civic",]
• x
• y<-mtcars["Camaro Z28",]
• y
• dist(rbind(x,y))
• dist(as.matrix(mtcars))](https://image.slidesharecdn.com/clustering-230223060645-015a12bd/85/Clustering-pptx-4-320.jpg)
![• data("iris")
• head(iris)
• nrow(iris)
• i1<-iris
• i1
• i1$Species=NULL
• head(i1)
• res=kmeans(i1,3)
• res
• plot(iris[c("Petal.Length","Petal.Width")],col=res$cluster)
• plot(iris[c("Petal.Length","Petal.Width")],col=iris$Species)
• table(iris$Species,res$cluster)
• plot(iris[c("Sepal.Length","Sepal.Width")],col=res$cluster)
• plot(iris[c("Sepal.Length","Sepal.Width")],col=iris$Species)](https://image.slidesharecdn.com/clustering-230223060645-015a12bd/85/Clustering-pptx-9-320.jpg)
Clustering.pptx
- 1. Clustering Clustering in R Programming Language is an unsupervised learning technique in which the data set is partitioned into several groups called as clusters based on their similarity. Several clusters of data are produced after the segmentation of data. All the objects in a cluster share common characteristics. During data mining and analysis, clustering is used to find similar datasets.
- 2. Applications of Clustering in R Programming Language • Marketing: In R programming, clustering is helpful for the marketing field. It helps in finding the market pattern and thus, helping in finding the likely buyers. Getting the interests of customers using clustering and showing the same product of their interest can increase the chance of buying the product. • Medical Science: In the medical field, there is a new invention of medicines and treatments on a daily basis. Sometimes, new species are also found by researchers and scientists. Their category can be easily found by using the clustering algorithm based on their similarities. • Games: A clustering algorithm can also be used to show the games to the user based on his interests. • Internet: An user browses a lot of websites based on his interest. Browsing history can be aggregated to perform clustering on it and based on clustering results, the profile of the user is generated.
- 3. Methods of Clustering • There are 2 types of clustering in R programming: • Hard clustering: In this type of clustering, the data point either belongs to the cluster totally or not and the data point is assigned to one cluster only. • The algorithm used for hard clustering is k-means clustering. • Soft clustering: In soft clustering, the probability or likelihood of a data point is assigned in the clusters rather than putting each data point in a cluster. • Each data point exists in all the clusters with some probability. • The algorithm used for soft clustering is the fuzzy clustering method or soft k-means.
- 4. Find The Distance • m<-matrix(1:16, nrow=4) • m • dist(m,method="euclidean") • dist(m,method="manhattan") • dist(m,method="maximum") • dist(m,method="canberra") • dist(m,method="minkowski") • x<-mtcars["Honda Civic",] • x • y<-mtcars["Camaro Z28",] • y • dist(rbind(x,y)) • dist(as.matrix(mtcars))
- 5. K-Means Clustering in R Programming language • K-Means is an iterative hard clustering technique that uses an unsupervised learning algorithm. • In this, total numbers of clusters are pre- defined by the user and based on the similarity of each data point, the data points are clustered. • This algorithm also finds out the centroid of the cluster.
- 6. • Specify number of clusters (K): Let us take an example of k =2 and 5 data points. • Randomly assign each data point to a cluster: In the below example, the red and green color shows 2 clusters with their respective random data points assigned to them. • Calculate cluster centroids: The cross mark represents the centroid of the corresponding cluster. • Re-allocate each data point to their nearest cluster centroid: Green data point is assigned to the red cluster as it is near to the centroid of red cluster. • Re-figure cluster centroid • Syntax: kmeans(x, centers, nstart) • where, • x represents numeric matrix or data frame object • centers represents the K value or distinct cluster centers • nstart represents number of random sets to be chosen
- 7. • install.packages("factoextra") • library(factoextra) • # Loading dataset • df <- mtcars • df • # Omitting any NA values • df <- na.omit(df) • df • # Scaling dataset • df <- scale(df) • df • # output to be present as PNG file • png(file = "KMeansExample.png") • km <- kmeans(df, centers = 4, nstart = 25) • km • # Visualize the clusters • fviz_cluster(km, data = df) • # saving the file • dev.off() • # output to be present as PNG file • png(file = "KMeansExample2.png") • km <- kmeans(df, centers = 5, nstart = 25) • km • # Visualize the clusters • fviz_cluster(km, data = df) • # saving the file • dev.off()
- 8. • # output to be present as PNG file • png(file = "KMeansExample2.png") • km <- kmeans(df, centers = 5, nstart = 25) • km • # Visualize the clusters • fviz_cluster(km, data = df) • # saving the file • dev.off()
- 9. • data("iris") • head(iris) • nrow(iris) • i1<-iris • i1 • i1$Species=NULL • head(i1) • res=kmeans(i1,3) • res • plot(iris[c("Petal.Length","Petal.Width")],col=res$cluster) • plot(iris[c("Petal.Length","Petal.Width")],col=iris$Species) • table(iris$Species,res$cluster) • plot(iris[c("Sepal.Length","Sepal.Width")],col=res$cluster) • plot(iris[c("Sepal.Length","Sepal.Width")],col=iris$Species)
- 13. Hierarchical Clustering in R Programming • Hierarchical clustering in R Programming Language is an Unsupervised non-linear algorithm in which clusters are created such that they have a hierarchy(or a pre- determined ordering). • For example, consider a family of up to three generations. A grandfather and mother have their children that become father and mother of their children. • So, they all are grouped together to the same family i.e they form a hierarchy. • R – Hierarchical Clustering • Hierarchical clustering is of two types: • Agglomerative Hierarchical clustering: It starts at individual leaves and successfully merges clusters together. Its a Bottom-up approach. • Divisive Hierarchical clustering: It starts at the root and recursively split the clusters. It’s a top-down approach.
- 14. • In hierarchical clustering, Objects are categorized into a hierarchy similar to a tree- shaped structure which is used to interpret hierarchical clustering models. The algorithm is as follows: • Make each data point in a single point cluster that forms N clusters. • Take the two closest data points and make them one cluster that forms N-1 clusters. • Take the two closest clusters and make them one cluster that forms N-2 clusters. • Repeat steps 3 until there is only one cluster.
- 16. • Dendrogram is a hierarchy of clusters in which distances are converted into heights. • It clusters n units or objects each with p feature into smaller groups. • Units in the same cluster are joined by a horizontal line. The leaves at the bottom represent individual units. • It provides a visual representation of clusters. Thumb Rule: Largest vertical distance which doesn’t cut any horizontal line defines the optimal number of clusters.
- 17. • The Dataset • mtcars(motor trend car road test) comprise fuel consumption, performance, and 10 aspects of automobile design for 32 automobiles. It comes pre-installed with dplyr package in R. • # Installing the package • install.packages("dplyr") • • # Loading package • library(dplyr) • • # Summary of dataset in package • head(mtcars)
- 18. • Performing Hierarchical clustering on Dataset • Using Hierarchical Clustering algorithm on the dataset using hclust() which is pre-installed in stats package when R is installed. • # Finding distance matrix • distance_mat <- dist(mtcars, method = 'euclidean') • distance_mat • The values are shown as per the distance matrix calculation with the method as euclidean. • Model Hierar_cl: • # Fitting Hierarchical clustering Model • # to training dataset • set.seed(240) # Setting seed • Hierar_cl <- hclust(distance_mat, method = "average") • Hierar_cl • In the model, the cluster method is average, distance is euclidean and no. of objects are 32.
- 19. • Plot dendrogram: • # Plotting dendrogram • plot(Hierar_cl) • • # Choosing no. of clusters • # Cutting tree by height • abline(h = 110, col = "green") • The plot dendrogram is shown with x-axis as distance matrix and y-axis as height. • Cutted tree: • # Cutting tree by no. of clusters • fit <- cutree(Hierar_cl, k = 3 ) • fit • So, Tree is cut where k = 3 and each category represents its number of clusters. • Plotting dendrogram after cutting: • table(fit) • rect.hclust(Hierar_cl, k = 3, border = "green") • The plot denotes dendrogram after being cut. The green lines show the number of clusters as per the thumb rule.