Get cluster points after KMeans in a list format

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Suppose I clustered a data set using sklearn's K-means.

I can see the centroids easily using KMeans.cluster_centers_ but I need to get the clusters as I get centroids.

How can I do that?

You need to do the following (see comments in my code):
import numpy as np
from sklearn.cluster import KMeans
from sklearn import datasets

np.random.seed(0)

# Use Iris data
iris = datasets.load_iris()
X = iris.data
y = iris.target

# KMeans with 3 clusters
clf =  KMeans(n_clusters=3)
clf.fit(X,y)

#Coordinates of cluster centers with shape [n_clusters, n_features]
clf.cluster_centers_

#Labels of each point
clf.labels_

# !! Get the indices of the points for each corresponding cluster
mydict = {i: np.where(clf.labels_ == i)[0] for i in range(clf.n_clusters)}

# Transform the dictionary into list
dictlist = []
for key, value in mydict.iteritems():
    temp = [key,value]
    dictlist.append(temp)

RESULTS

{0: array([ 50,  51,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
            64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
            78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
            91,  92,  93,  94,  95,  96,  97,  98,  99, 101, 106, 113, 114,
           119, 121, 123, 126, 127, 133, 138, 142, 146, 149]),
 1: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
           17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
           34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),
 2: array([ 52,  77, 100, 102, 103, 104, 105, 107, 108, 109, 110, 111, 112,
           115, 116, 117, 118, 120, 122, 124, 125, 128, 129, 130, 131, 132,
           134, 135, 136, 137, 139, 140, 141, 143, 144, 145, 147, 148])}


[[0, array([ 50,  51,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
             64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
             78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
             91,  92,  93,  94,  95,  96,  97,  98,  99, 101, 106, 113, 114,
             119, 121, 123, 126, 127, 133, 138, 142, 146, 149])],
 [1, array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])],
 [2, array([ 52,  77, 100, 102, 103, 104, 105, 107, 108, 109, 110, 111, 112,
             115, 116, 117, 118, 120, 122, 124, 125, 128, 129, 130, 131, 132,
             134, 135, 136, 137, 139, 140, 141, 143, 144, 145, 147, 148])]]

Find Cluster Diameter and Associated Cluster Points with KMeans , #iris example iris = datasets.load_iris() x = iris.data y = iris.target estimator To get the clusters' radii you can use the following code snippet: and pick the maximum which is the radius of that cluster''' for cluster in list(set(y)):  import numpy as np from sklearn.cluster import KMeans from sklearn import datasets np.random.seed(0) # Use Iris data iris = datasets.load_iris() X = iris.data y = iris.target # KMeans with 3 clusters clf = KMeans(n_clusters=3) clf.fit(X,y) #Coordinates of cluster centers with shape [n_clusters, n_features] clf.cluster_centers_ #Labels of each point clf.labels_ # !! Get the indices of the points for each corresponding cluster mydict = {i: np.where(clf.labels_ == i)[0] for i in range(clf.n

You probably look for the attribute labels_.

K-Means Clustering with Scikit-Learn, The K-means algorithm starts by randomly choosing a centroid value for each cluster. After that the algorithm iteratively performs three steps: (i) Find the Euclidean to see how the K-means algorithm works with the help of a handcrafted example, After assigning data points to the corresponding clusters, the next step is to  After each data point is assigned to a cluster, reassign the centroid value for each cluster to be the mean value of all the data points within the cluster. m i (t + 1) = 1 | S i (t) | ∑ x j ∈ S i (t) x j Reassign data points to new clusters. This is where the iterative process begins.

It's been very long asked question so I think you already have the answer but let me post as someone can be benefited from it. We can get cluster points by just using its centroid. Scikit-learn has an attribute called cluster_centers_ which returns n_clusters and n_features. The very simple code that you can see it below that to describe the cluster center and please go through all the comments in the code.

import numpy as np
from sklearn.cluster import KMeans
from sklearn import datasets
from sklearn.preprocessing import StandardScaler

# Iris data
iris = datasets.load_iris()
X = iris.data
# Standardization
std_data = StandardScaler().fit_transform(X)

# KMeans clustering with 3 clusters
clf =  KMeans(n_clusters = 3)
clf.fit(std_data)

# Coordinates of cluster centers with shape [n_clusters, n_features]
# As we have 3 cluster with 4 features
print("Shape of cluster:", clf.cluster_centers_.shape)

# Scatter plot to see each cluster points visually 
plt.scatter(std_data[:,0], std_data[:,1], c = clf.labels_, cmap = "rainbow")
plt.title("K-means Clustering of iris data flower")
plt.show()

# Putting ndarray cluster center into pandas DataFrame
coef_df = pd.DataFrame(clf.cluster_centers_, columns = ["Sepal length", "Sepal width", "Petal length", "Petal width"])
print("\nDataFrame containg each cluster points with feature names:\n", coef_df)

# converting ndarray to a nested list 
ndarray2list = clf.cluster_centers_.tolist()
print("\nList of clusterd points:\n")
print(ndarray2list)

OUTPUTS: This is the output of the above code.

Grouping data points with k-means clustering., Home · About · Data Science · Reading List · Quotes · Life · Favorite Talks K-​means clustering is a simple method for partitioning n data points in k groups, or clusters. clustering, on a sample of your data set and using the resultant cluster centroids as Translating this to English, we get the following:. 2. You can cluster the points using K-means and use the cluster as a feature for supervised learning. It is not always necessary that the accuracy will increase. It may increase or might decrease as well. You can try and check that out. Also, when you have an imbalanced dataset, accuracy is not the right evaluation metric to evaluate your model.

sklearn.cluster.KMeans Python Example, This page provides Python code examples for sklearn.cluster.KMeans. The following are code examples for showing how to use sklearn.cluster. means = 8): kk = KMeans(n_clusters = means) kk.fit(points) labels = kk.predict(points) r 10) n_init = kwargs.get('n_init', 10) #set up the clustering function estimator = cluster. I have done clustering using Kmeans using sklearn. While it has a method to print the centroids, I am finding it rather bizzare that scikit-learn doesn't have a method to find out the cluster length (or that I have not seen it so far). Is there a neat way to get the cluster-length of each cluster or many points associated with cluster?

K-Means Clustering with scikit-learn, Learn about the inner workings of the K-Means clustering algorithm with an To do this, you will need a sample dataset (training set): After choosing the centroids, (say C1 and C2) the data points I will list the feature names for you: Let's get the total number of missing values in both datasets. K-means clustering is one of the most commonly used unsupervised machine learning algorithm for partitioning a given data set into a set of k groups. In this tutorial, you will learn: 1) the basic steps of k-means algorithm; 2) How to compute k-means in R software using practical examples; and 3) Advantages and disavantages of k-means clustering

Clustering with K-means, In case of K-means Clustering, we are trying to find k cluster centres as the mean To do this, instead of working with the entire dataset, we draw up a sample and run Scaling is also important from a clustering perspective as the distance between points affects the way colNames = list(cols_of_interest) This centroid might not necessarily be a member of the dataset. This is an iterative clustering algorithms in which the notion of similarity is derived by how close a data point is to the centroid of the cluster. k-means is a centroid based clustering, and will you see this topic more in detail later on in the tutorial.

Comments
  • Thanks for your response but i need to see each cluster with it's data points as a list. How can i do that?
  • can you write a sample code? It will be very helpful for me.