Python: Data Analytics and Visualization by Phuong Vo.T.H & Martin Czygan & Ashish Kumar & Kirthi Raman

Author:Phuong Vo.T.H & Martin Czygan & Ashish Kumar & Kirthi Raman [Vo.T.H, Phuong]
Language: eng
Format: azw3
Publisher: Packt Publishing
Published: 2017-03-31T04:00:00+00:00

The goal of this algorithm is to attain a configuration of cluster centers and cluster observation so that the overall J squared error function or J-score is minimized:

Here, c=number of clusters, ci=number of points in the cluster, and Vi=centroid of the ith cluster.

The J squared error function can be understood as the sum of the squared distance of points from their respective cluster centroids. A smaller value of J squared function implies tightly packed and homogeneous clusters. This also implies that most of the points have been placed in the right clusters.

Let us try the k-means clustering algorithm for clustering some random numbers between 0 and 1. The Python library and Scipy have some inbuilt methods to perform the algorithm and return a list defining which observation belongs to which cluster:

Define a set of observations consisting of random numbers ranging from 0 to 1. In this case, we have defined an observation set of 30x3:Import numpy as np obs=np.random.random(90).reshape(30,3) obs

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