Pass Task 7.1P: K-Means and Hierarchical Clustering

Task description:

In machine learning, clustering is used for analyzing and grouping data which does not include pre-labelled class or even a class attribute at all. K-Means clustering and hierarchical clustering are all unsupervised learning algorithms.

K- means is a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters. It is a division of objects into clusters such that each object is in exactly one cluster, not several.

In Hierarchical clustering, clusters have a tree like structure or a parent child relationship. Here, the two most similar clusters are combined together and continue to combine until all objects are in the same cluster.

In this task, you use K-Means and Agglomerative Hierarchical algorithms to cluster a synthetic dataset and compare their difference.

You are given:

• np.random.seed(0)

• make_blobs class with input:

o n_samples: 200

o centers: [3,2], [6, 4], [10, 5]

o cluster_std: 0.9

• KMeans() function with setting: init = "k-means++", n_clusters = 3, n_init = 12

• AgglomerativeClustering() function with setting: n_clusters = 3, linkage = 'average'

• Other settings of your choice

You are asked to:

• plot your created dataset

• plot the two clustering models for your created dataset

• set the K-Mean plot with title “KMeans”

• set the Agglomerative Hierarchical plot with title “Agglomerative Hierarchical”

• calculate distance matrix for Agglomerative Clustering using the input feature matrix (linkage = complete)

• display dendrogram

Sample output as shown in the following figure is for demonstration purposes only. Yours might be different from the provided.