- Planerative Cluster Task Description In Machine Learning Clustering Is Used For Analyzing And Grouping Data Which Do 1 (31.76 KiB) Viewed 25 times
- Planerative Cluster Task Description In Machine Learning Clustering Is Used For Analyzing And Grouping Data Which Do 2 (60.92 KiB) Viewed 25 times
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(o) make_blobs class with input: on_samples: 200 o centers: (3,2), (6,4), (10,5) o cluster_std: 0.9 • Means() 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 "Means" • 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.