Question

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: \begin{itemize} \item np.random.seed(0) \item make_blobs class with input: \begin{itemize} \item n_samples: 200 \item centers: [3,2], [6, 4], [10, 5] \item cluster_std: 0.9 \end{itemize} \item KMeans() function with setting: init = "k-means++", n_clusters = 3, n_init = 12 \item AgglomerativeClustering() function with setting: n_clusters = 3, linkage = 'average' \item Other settings of your choice \end{itemize} You are asked to: \begin{itemize} \item plot your created dataset \item plot the two clustering models for your created dataset \item set the K-Mean plot with title "KMeans" \item set the Agglomerative Hierarchical plot with title "Agglomerative Hierarchical" \item calculate distance matrix for Agglomerative Clustering using the input feature matrix (linkage = complete) \item display dendrogram \end{itemize}

          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:
\begin{itemize}
    \item np.random.seed(0)
    \item make_blobs class with input:
        \begin{itemize}
            \item n_samples: 200
            \item centers: [3,2], [6, 4], [10, 5]
            \item cluster_std: 0.9
        \end{itemize}
    \item KMeans() function with setting: init = "k-means++", n_clusters = 3, n_init = 12
    \item AgglomerativeClustering() function with setting: n_clusters = 3, linkage = 'average'
    \item Other settings of your choice
\end{itemize}
You are asked to:
\begin{itemize}
    \item plot your created dataset
    \item plot the two clustering models for your created dataset
    \item set the K-Mean plot with title "KMeans"
    \item set the Agglomerative Hierarchical plot with title "Agglomerative Hierarchical"
    \item calculate distance matrix for Agglomerative Clustering using the input feature matrix
    (linkage = complete)
    \item display dendrogram
\end{itemize}

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)

* makeblobs class with input:

* nsamples: 200

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

* clusterstd: 0.9

* KMeans() function with setting: init = "k-means++", nclusters = 3, ninit = 12

* AgglomerativeClustering() function with setting: nclusters = 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

Added by James S.

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