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Chessboard distance or Minkowski distance

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Consider two objects and describe by 10 features and write a c-program to compute Lr distance by the varying the “r-value”. The C-program automatically reads the 10 random number and the value of r going to increment automatically up to 50. Our infinity value is set to 50, can be changed to the desired value. the r-value is 1 then it is called the city-block distance, if the value of r is 2 then it is called Euclidean distance and if the value of r is infinity then it is called as a chessboard or Lr distance, supremum distance (also referred to as L max , L ∞ norm and as the Chebyshev distance ) is a generalization of the Minkowski distance for L → ∞. The C-program automatically reads the 10 random number and the value of r going to increment automatically up to 50. Our infinity value is set to 50, can be changed to the desired value. The c-program is as follows: #include<stdio.h> #include<stdlib.h> #include<math.h> #include<floa...
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Finding the Area of a polygon

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Area of a polygon Abstract — This work discusses finding out the are of a given polygon with n number of edges or sides. The time complexity and scalability of the algorithm are analyzed. The results indicate that the algorithm is scalable and efficient. Keywords: polygon, triangles, polygon. I.       Introduction Area of a given polygon with n number of edges can be found by different methods like finding the centroid of a given polygon then apply area formula to get the area of that polygon. Using the divide and conquer technique to find the area of a given polygon, this technique is used when the given problem is larger or more complex then divide the problem until we get smaller subproblems than find solutions for that smaller subproblems then combine the results of smaller subproblems. Now we obtain the result for a complex problem. To find the area for a triangle (a simple polygon) we know the formula to find the area. Area =  ½...
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Techniques of dimensionality reduction

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Techniques of dimensionality reduction Dimensionality reduction:- Reducing the number of features keeping in mind the ability to discriminating each data object from the rest of the objects. Given are "m" number of features (m relatively large) discriminating each "n" objects, the task is to re-describing "n" objects with d<<m (d is relatively smaller than m) number of features which may be some of those "m" features or may be new features computed using "m" features, such that desired (target) or task accomplished (classification, clustering) without loss of data generality. Finding out d<<m choosing some d out of m features (also called sub-setting or selection). computed d from m (also called feature transformation). Fig: Dimensionality reduction 1. Feature sub-setting:- Thanks for reading. Subscribe and follow to get notification of my blog's update...
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