What Is the Center Selection Problem in Geometry?

In summary, the author is discussing how to find the center of a circle if there are N centers and the task is to find the center of the circle that minimizes the distance between any two sites within the circle.
  • #1
zak100
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TL;DR Summary
I am trying to understand the center selection problem but I have some problems in understanding the fundamental things.
Hi
I have some questions about the center selection problem:
1) K= 4, Are we finding the center of 4 circles?
2) r(C) = maxi dist(si, C) = smallest covering radius.
In case of r(C), I can’t understand what is meant by ##max_i dist(s_i, C) ##
How the max will work?

Some body please guide me.

Center slection problem.jpg

Zulfi.
 
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  • #2
zak100 said:
1) K= 4, Are we finding the center of 4 circles?
Yes, if ##k=4## you need to find the center of 4 circles. ##k## is the number of centers, so the number of circles.

zak100 said:
2) r(C) = maxi dist(si, C) = smallest covering radius.
In case of r(C), I can’t understand what is meant by ##max_i dist(s_i, C) ##
How the max will work?
The function ##\mathrm{dist}(s_i, C)## should return the distance between site ##s_i## and the nearest center ##C##. Then you find the maximum of all the distances thus calculated, and that becomes ##r(C)##. The task is then to find where to place the centers to minimize ##r(C)##.

Note that in the picture, ##\max_i \mathrm{dist}(s_i, C)## corresponds to two sites from the upper left circle, where you have sites just on the edge of the circle.
 
  • #3
Hi,
Thanks for your response.
<Then you find the maximum of all the distances thus calculated, and that becomes r(C).>
(1)How may distances are returned by ##dist(s_i, C) ?##
(2)Why we don't find the minimum distance directly from the value return by the distance function?
Book says:
<
we define the distance of a site s to the centers as ##dist(s, C) = min_{c∈C} dist(s, c)##.
We say that C forms an r-cover if each site is within distance at most r from one of the centers—that is, if dist(s, C) ≤ r for all sites s ∈ S. The minimum r for which C is an r-cover will be called the covering radius of C and will be denoted by r(C). In other words, the covering radius of a set of centers C is the farthest that anyone needs to travel to get to his or her nearest center.>

Kleinberg, Jon. Algorithm Design (p. 607). Pearson Education. Kindle Edition.
(3)What is the difference between 'c' and 'C'?
(4)The function ##dist(s_i,C) \text{ should return the distance between site } s_i## and the nearest center C.
Sorry I can't understand the sentence ## \text{"between the site } s_i \text{ and the nearest center C" }##. Are we considering the point within the site or on the edge?
(5)Why the book uses the word "farthest" if we are interested in finding out the closest distance.
Some body please guide me.
Zulfi.
 
Last edited:
  • #4
zak100 said:
(1)How may distances are returned by ##dist(s_i, C) ?##
One. As the book itself says, you have ##dist(s_i, C) = min_{c \in C} dist(s_i, c)##.

zak100 said:
(2)Why we don't find the minimum distance directly from the value return by the distance function?
I'm not sure what you are asking here. Yes, ##dist(s_i, C)## is found as the minimum distance. But the work here is to optimize ##C## such that ##max_i dist(s_i, C)## is minimum.

zak100 said:
(3)What is the difference between 'c' and 'C'?
##c## is one center while ##C## is the set of all centers.

zak100 said:
(4)The function ##dist(s_i,C) \text{ should return the distance between site } s_i## and the nearest center C.
Sorry I can't understand the sentence ## \text{"between the site } s_i \text{ and the nearest center C" }##. Are we considering the point within the site or on the edge?
This is simply a reformulation of ##dist(s_i, C) = min_{c \in C} dist(s_i, c)##. You need a function ##dist(s_i, C)## that will calculate the distance between ##s_i## and all the centers, and return the smallest of these values, which is then simply the distance between ##s_i## and the nearest center.

zak100 said:
(5)Why the book uses the word "farthest" if we are interested in finding out the closest distance.
Because you are minimizing a maximum. You want the smallest possible travel distance for the site that is farthest away from its nearest center.
 
  • #5
Hi,

Thanks. God bless you.

Zulfi.
 

What is the center selection problem?

The center selection problem is a problem in statistics and data analysis where the goal is to identify a representative center or central point in a dataset. This center point should accurately reflect the overall data and can be used for various purposes such as clustering, classification, and outlier detection.

What are the basic steps involved in solving the center selection problem?

The basic steps involved in solving the center selection problem are:

  1. Defining the objective or purpose of identifying a center point
  2. Choosing an appropriate distance metric to measure the similarity between data points
  3. Selecting a suitable algorithm or method for identifying the center point
  4. Evaluating the results and adjusting parameters if necessary

What are some common distance metrics used in the center selection problem?

Some common distance metrics used in the center selection problem are Euclidean distance, Manhattan distance, and Mahalanobis distance. These metrics measure the distance between two data points based on their numerical values and can be used for both numerical and categorical data.

What are some popular algorithms for solving the center selection problem?

Some popular algorithms for solving the center selection problem are k-means clustering, hierarchical clustering, and density-based clustering. These algorithms use different approaches to identify the center point, such as partitioning the data into clusters or finding the point with the highest density.

What are the applications of the center selection problem?

The center selection problem has various applications in data analysis, including:

  • Clustering: Identifying the center point of a cluster can help in grouping similar data points together.
  • Classification: The center point can be used as a representative feature to classify new data points.
  • Outlier detection: The center point can help in identifying and removing outliers that do not fit the overall data pattern.
  • Data visualization: The center point can be used to visualize and summarize a dataset, providing insights into the data distribution.

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