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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.
Each question, by default, should be solved INDIVIDUALLY, unless marked as \collaborative". Questions marked as \collaborative" implies that for those questions you are encourage
A. Compute descriptive statistics for each stock and the S&P 500. Comment on your results. Which stocks are most volatile?
Mathematical Properties The sum of deviations of the items from the arithmetic mean (taking signs into account) is always zero, i.e. = 0. The sum of
Cluster Analysis could be also represented more formally as optimization procedure, which tries to minimize the Residual Sum of Squares objective function: where μ(ωk) - is a centr
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For many decades, there has been considerable attention paid to identifying various factors that help to reduce the number of fatalities on Australian roads. In 1964 Victoria and S
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