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O In a population map of the U over 1 million. O In a population map of the U

O In a population map of the U over 1 million. O In a population map of the U

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
Problem 1PE
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**Explaining K-Means Clustering with Graphs** In a Cartesian graph with many points plotted, the k-means clustering algorithm finds the best clustering of points as shown: ### Diagram Explanation: - **Before K-Means**: Points are scattered randomly across the graph without any clear grouping. - **After K-Means**: Points are divided into three distinct clusters, each outlined by an ellipse. The clusters are differentiated by color: - Green cluster - Blue cluster - Black cluster Each cluster groups points that are closer to each other in the graph space, representing a pattern or a category found by the algorithm. ### Application of K-Means Clustering: Question: Which of the following scenarios does it make sense to apply k-means clustering to? - ○ In a population map of the US, estimate how many cities there are with a population over 1 million. - ○ In a population map of the US, estimate the population of a given city. - ○ In a population map of the US, determine where to best place cell towers to cover the most population. - ○ In a population map of the US, determine where the most popular cities are. **Answer**: K-means clustering is best suited for the scenario of determining where to best place cell towers (third option) as it helps to identify dense clusters of population, optimizing coverage.
Transcribed Image Text:**Explaining K-Means Clustering with Graphs** In a Cartesian graph with many points plotted, the k-means clustering algorithm finds the best clustering of points as shown: ### Diagram Explanation: - **Before K-Means**: Points are scattered randomly across the graph without any clear grouping. - **After K-Means**: Points are divided into three distinct clusters, each outlined by an ellipse. The clusters are differentiated by color: - Green cluster - Blue cluster - Black cluster Each cluster groups points that are closer to each other in the graph space, representing a pattern or a category found by the algorithm. ### Application of K-Means Clustering: Question: Which of the following scenarios does it make sense to apply k-means clustering to? - ○ In a population map of the US, estimate how many cities there are with a population over 1 million. - ○ In a population map of the US, estimate the population of a given city. - ○ In a population map of the US, determine where to best place cell towers to cover the most population. - ○ In a population map of the US, determine where the most popular cities are. **Answer**: K-means clustering is best suited for the scenario of determining where to best place cell towers (third option) as it helps to identify dense clusters of population, optimizing coverage.
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