Detecting a Torus in a data cloud

In summary, the conversation is discussing a method of uniformly distributing points in a data cloud and creating edges between points with a certain metric value. The resulting graph would indicate density, and the method could potentially be used for topological data analysis. However, the question is not well-defined and it is suggested to repost in a different forum for better clarification and discussion.
  • #1
FallenApple
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Would the following method work? I could uniformly distribute points into the data cloud. Of the "darts" that I threw in, create edges between all points with a metric value under a certain amount. The nodes in the resulting graph that have more neighbors would indicate greater density. I could tune the amount of darts and since the connective density would increase in a more non linear fashion for denser regions vs less dense regions, I could tell if there's a cavity or not.

Would this work in practice?

Would it work topologically? That is, I would get the same indication for a coffee mug cloud vs a doughnut? I mean, regardless of the shape, I would end up with two less dense regions.
 
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  • #2
This is not my area and it seems to be confusing for other mentors, too. In an attempt to get some clarity, could you please provide a considerably more precise sample problem you are trying to solve?

My goal is to get you an answer - good clear questions get answers, fuzzy or overly general questions get more questions.
 
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  • #3
jim mcnamara said:
This is not my area and it seems to be confusing for other mentors, too. In an attempt to get some clarity, could you please provide a considerably more precise sample problem you are trying to solve?

My goal is to get you an answer - good clear questions get answers, fuzzy or overly general questions get more questions.
In the meantime, best I can think is that this is from Persistent Homology and, more generally, from Topological Data Analysis.
 
  • #4
WWGD said:
In the meantime, best I can think is that this is from Persistent Homology and, more generally, from Topological Data Analysis.
In this case it would be better placed in the computing forum, isn't it?
 
  • #5
fresh_42 said:
In this case it would be better placed in the computing forum, isn't it?
Yes, it seems like it would be a better fit there, tho we may make a nonclustered index and include a link to it from here if possible, I would think.
 
  • #6
Is this essentially the same problem as in image enhancement and image recognition ? These subjects have been extensively researched and there is a large amount of published material available about both the mathematical theory and practical computation methods .
 
  • #7
Nidum said:
Is this essentially the same problem as in image enhancement and image recognition ? These subjects have been extensively researched and there is a large amount of published material available about both the mathematical theory and practical computation methods .
Not sure, sorry, but sounds right/close. The dart thing suggests a Monte Carlo method for pattern recognition.
 
  • #8
Since the OP is obviously not well formulated, such that meanwhile everyone guesses what might have been meant, I close this thread. It makes no sense to start a discussion on speculations. The more as it is not clear, if we're right.

@FallenApple If you repost this, please make sure that you're understood and place it in an appropriate forum. If it's computation, then it shouldn't be here.
 

1. What is a torus and how does it relate to data clouds?

A torus is a geometric shape that resembles a donut or inner tube. In data analysis, a torus can be used to represent a circular data distribution in a multi-dimensional space. This is useful for detecting patterns or clusters in large data sets.

2. How do you detect a torus in a data cloud?

To detect a torus in a data cloud, you can use various algorithms such as density-based clustering or principal component analysis. These techniques analyze the data points and identify the underlying circular pattern that represents a torus.

3. What are the benefits of detecting a torus in a data cloud?

Detecting a torus in a data cloud can help reveal hidden relationships or patterns in the data, which can lead to better insights and decision making. It can also aid in data visualization and simplification, making it easier to interpret complex data sets.

4. Are there any challenges or limitations to detecting a torus in a data cloud?

One challenge is that detecting a torus requires a large enough data set and may not be effective for small or sparse data sets. Additionally, the results may vary depending on the chosen algorithm and the interpretation of the data may be subjective.

5. How can detecting a torus in a data cloud be applied in different fields of science?

Detecting a torus in a data cloud has various applications in different fields of science such as astronomy, physics, and biology. It can be used to identify patterns in celestial objects, analyze the structure of molecules, and study the distribution of species in an ecosystem, among others.

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