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There exists an unclassified data set with hidden data structures in it. The task in this assignment is to perform comprehensive Cluster Analysis in order to reveal the structures and similar data groups.
1. Implement a simple K-means method, which is able to handle real values data in attributes. Also you need to add functionality in your program that allows utilization of Euclidean, City Block, Euclidean Squared and Chebyshev distances. You are free to use any kind of weights (for feature or data instance) in the program if necessary.
2. Find unlabeled data set test.txt and initial centroids data set centroids.txt in the archive, both files have the following format: [attribute1_value attribute2_value ... attribute90_value]. The unlabeled data set includes 350 samples and the initial centroids set consists of 15 samples. Data instances in both files have 90 attributes.
A monopolist firm''s demand curve is given by P:100-2q. (a) Find its marginal revenue function.
Betting on sporting events is big business both in the US and abroad. Consider, for instance, next winter’s American football tournament known as the Superbowl. Billions of dollars
Q. Compute the output of correlation? The following figure shows (a) a 3-bit image of size 5-by-5 image in the square, with x and y coordinates specified, (b) a Laplacian
The project of building a backyard swimming pool consists of eight major activities and has to be completed within 19 weeks. The activities and related data are given in the follow
The calculations of arithmetic mean may be simple and foolproof, but the application of the result may not be so foolproof. An arithmetic mean may not merely lack
The file Midterm Data.xls has a tab labeled "Many vs. S&P" which presents historical price data for several assets, a volatility condition (VIDX = 1 if the NYSE volatility is grea
An approximation to the error of a Riemannian sum: where V g (a; b) is the total variation of g on [a, b] de ned by the sup over all partitions on [a, b], including (a; b
Factor analysis (FA) explains variability among observed random variables in terms of fewer unobserved random variables called factors. The observed variables are expressed in
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