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1. K-Mean++. Implement K-Mean++ clustering algorithm in python as follows:
– Read input file ‘as4_1.txt’ given in the Canvas course website. The file is composed of X and Y values in the first and second columns and label in the third column.
– Create myInit() that places the initial k centroids far away from each other in the
4 steps as shown below:
1. Randomly select the first centroid from the data points
2. For each data point compute its distance from the nearest, previously chosen centroid
Use following Euclidean distance function: import numpy as np def euclidean2D(point1, point2):
x1 = point1
x2 = point2
y1 = point1
y2 = point2 return np.sqrt((x1 – x2)**2 + (y1 – y2)**2)
3. Select the point having maximum distance from the nearest centroid as the next centroid
4. Repeat steps 2 and 3 until k centroids have been sampled
– Create myAssign() that assigns each example to the nearest centroid
– Create myCentroid() that calculates a new centroid of all points that are assigned to the same centroid.
– Create myUpdateCentroid() that moves the centroids to the center of the examples that were assigned to it
– Create myKmeanPlusPlus() that initially calls myInit(), and then repeats to call myAssign(), myCentroid(), and myUpdateCentroids() until the cluster assignments do not change or a user-defined tolerance or maximum number of iteration is reached. myKmeanPlusPlus() should ask user to receive the following arguments and use the same variable name in the parenthesis:
1. The number of clusters (k)
2. Tolerance (myTol)
3. Maximum number of iterations (myMax) myKmeanPlusPlus()returns a list of new labels.
– Create myPlot() that visualizes plot of clustering result in different colors and markers. You can use any plot method.
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Posted on : December 20th, 2019
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