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Which one of the following statements best describes k-means clustering and its utility? K-means clustering reduces the amount of variables in the model to determine which features have the most variability. Press 1 K-means clustering groups data into relatively distinct groups by using a pre- determined number of clusters and iterating cluster assignments. Press 2 K-means clustering is a supervised learning algorithm that predicts continuous variables as a function of categorical variables (called clusters). Press 3 K-means clustering is a supervised learning algorithm that predicts categorical variables (called clusters) as a function of continuous variables. Press 4

Which one of the following statements best describes k-means clustering and its utility? K-means clustering reduces the amount of variables in the model to determine which features have the most variability. Press 1 K-means clustering groups data into relatively distinct groups by using a pre- determined number of clusters and iterating cluster assignments. Press 2 K-means clustering is a supervised learning algorithm that predicts continuous variables as a function of categorical variables (called clusters). Press 3 K-means clustering is a supervised learning algorithm that predicts categorical variables (called clusters) as a function of continuous variables. Press 4

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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Multiple Choice Which one of the following statements best describes k-means clustering and its utility? K-means clustering reduces the amount of variables in the model to determine which features have the most variability. Press 1 K-means clustering groups data into relatively distinct groups by using a pre- determined number of clusters and iterating cluster assignments. Press 2 K-means clustering is a supervised learning algorithm that predicts continuous Press 3 variables as a function of categorical variables (called clusters). K-means clustering is a supervised learning algorithm that predicts categorical variables (called clusters) as a function of continuous variables. Press 4
Transcribed Image Text:Multiple Choice Which one of the following statements best describes k-means clustering and its utility? K-means clustering reduces the amount of variables in the model to determine which features have the most variability. Press 1 K-means clustering groups data into relatively distinct groups by using a pre- determined number of clusters and iterating cluster assignments. Press 2 K-means clustering is a supervised learning algorithm that predicts continuous Press 3 variables as a function of categorical variables (called clusters). K-means clustering is a supervised learning algorithm that predicts categorical variables (called clusters) as a function of continuous variables. Press 4
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