Write a program using K-Means and Expectation Maximization clustering for the gi

Question

Write a program using K-Means and Expectation Maximization clustering for the given dataset. You have been assigned a dataset.

How to use this dataset in R:

Package Name: cluster.datasets

install.packages("cluster.datasets")

library(cluster.datasets)

data(paste your data set name)

your dataset name

data(mammal.dentition)

Program 1: K-Means Clustering

Q1: Find optimal number of clusters. (Hint: Elbow Method)

Q2: Apply K-Means

Q3: Visualization for K-Means Clustering

Program 2: Expectation Maximization Clustering

Q1: Apply Expectation Maximization

Q2: Visualization for Expectation Maximization

Write a program using K-Means and Expectation Maximization clustering for the given dataset. You have been assigned a dataset.

How to use this dataset in R:

Package Name: cluster.datasets

install.packages("cluster.datasets")

library(cluster.datasets)

data(paste your data set name)

your dataset name

data(mammal.dentition)

Program 1: K-Means Clustering

Q1: Find optimal number of clusters. (Hint: Elbow Method)

Q2: Apply K-Means

Q3: Visualization for K-Means Clustering

Program 2: Expectation Maximization Clustering

Q1: Apply Expectation Maximization

Q2: Visualization for Expectation Maximization

Explanation / Answer

import java.util.ArrayList;
import java.util.List;

public category Cluster purpose centroid;
   public int id;
  
   //Creates a brand new Cluster
   public Cluster(int id)

   public List getPoints() come points;
   }
  
   public void addPoint(Point point)

   public void setPoints(List points)

   public purpose getCentroid() come centroid;
   }

   public void setCentroid(Point centroid) center of mass;
   }

   public int getId()
  
   public void clear()
  
   public void plotCluster()
       System.out.println("]");
   }

}
Point.java

package com.dataonfocus.clustering;

import java.util.ArrayList;
import java.util.List;
import java.util.Random;

public category purpose personal double x = 0;
non-public double y = 0;
non-public int cluster_number = 0;

public Point(double x, double y)
  
  
public void setX(double x)
  
public double getX() come back this.x;
}
  
public void setY(double y)
  
public double getY() come back this.y;
}
  
public void setCluster(int n)
  
public int getCluster() come back this.cluster_number;
}
  
//Calculates the space between 2 points.
protected static double distance(Point p, purpose centroid) come back science.sqrt(Math.pow((centroid.getY() - p.getY()), 2) + Math.pow((centroid.getX() - p.getX()), 2));
}
  
//Creates random purpose
protected static purpose createRandomPoint(int min, int max) come back new Point(x,y);
}
  
protected static List createRandomPoints(int min, int max, int number)
   come points;
}
  
public String toString() come "("+x+","+y+")";
}
}
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package com.dataonfocus.clustering;

import java.util.ArrayList;
import java.util.List;
import java.util.Random;

public category purpose {

non-public double x = 0;
non-public double y = 0;
non-public int cluster_number = 0;

public Point(double x, double y)
  
  
public void setX(double x)
  
public double getX() come back this.x;
}
  
public void setY(double y)
  
public double getY() come back this.y;
}
  
public void setCluster(int n)
  
public int getCluster() come back this.cluster_number;
}
  
//Calculates the space between 2 points.
protected static double distance(Point p, purpose centroid) come back science.sqrt(Math.pow((centroid.getY() - p.getY()), 2) + Math.pow((centroid.getX() - p.getX()), 2));
}
  
//Creates random purpose
protected static purpose createRandomPoint(int min, int max) come back new Point(x,y);
}
  
protected static List createRandomPoints(int min, int max, int number)
   come points;
}
  
public String toString() come "("+x+","+y+")";
}
}

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