**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 clusterEach 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.

1. clustering algorithm cannot be used for estimation and hence this question is not answerable…

Answered: O In a population map of the U over 1…

Engineering

Computer Science

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

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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Problems on Growing Functions

For a given set of functions f and g, the growth of functions states that the function g grows as fast as the function f. The growth rate of algorithms helps determine the algorithm's efficiency and compare its performance to other algorithms.

Question

**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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