Book Recommendation for Nearest Neighbor Graphs

In summary, the conversation is about a person's difficulty understanding a paper on cluster identification in nearest neighbor graphs, and their search for an introductory book on this topic. They also inquire about other areas of math, such as statistics and probability, that may cover this topic. Some book recommendations are provided as potential resources for understanding these graphs.
  • #1
Bingk1
16
0
Hello,

I'm trying to read a paper titled "Cluster Identification in Nearest Neighbor Graphs". It's a mixture of probability, graph theory, and topology. I'm having difficulty interpreting some of the ideas, specially when it comes to k nearest neighbor graphs.
I've been trying to look for a book that is a sort of "introduction" to these types of graphs, but haven't been able to find any. Most are either too basic, or too advanced. Could someone recommend some literature on this subject?

Thanks!

P.S. I have been looking for graph theory books that cover this topic, but it just occurred to me that this topic (which is about graphs) might be better covered by another area of math (maybe statistics/probability), if so, could someone recommend what area of math I should be searching under. Thanks!
 
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  • #2
Bingk said:
Hello,

I'm trying to read a paper titled "Cluster Identification in Nearest Neighbor Graphs". It's a mixture of probability, graph theory, and topology. I'm having difficulty interpreting some of the ideas, specially when it comes to k nearest neighbor graphs.
I've been trying to look for a book that is a sort of "introduction" to these types of graphs, but haven't been able to find any. Most are either too basic, or too advanced. Could someone recommend some literature on this subject?

Thanks!

P.S. I have been looking for graph theory books that cover this topic, but it just occurred to me that this topic (which is about graphs) might be better covered by another area of math (maybe statistics/probability), if so, could someone recommend what area of math I should be searching under. Thanks!

Hi Bingk, :)

Maybe you will find the following books useful.

Computational Geometry: An Introduction (Monographs in Computer Science): Franco P. Preparata,Michael Ian Shamos: 9780387961316: Amazon.com: Books

Algorithms in Combinatorial Geometry (Eatcs Monographs on Theoretical Computer Science): H. Edelsbrunner,Herbert Edelsbrunner: 9780387137223: Amazon.com: Books

Kind Regards,
Sudharaka.
 

Related to Book Recommendation for Nearest Neighbor Graphs

1. What is a nearest neighbor graph?

A nearest neighbor graph is a type of undirected graph that shows the relationship between a set of data points based on their closest neighbors. Each data point is represented as a node, and edges are drawn between nodes if they are considered nearest neighbors.

2. How are nearest neighbor graphs used in book recommendations?

Nearest neighbor graphs are used in book recommendations by analyzing the reading patterns of users and identifying similar patterns among different books. This allows for more personalized and accurate book recommendations based on a user's preferences and reading history.

3. What are the benefits of using nearest neighbor graphs for book recommendations?

The benefits of using nearest neighbor graphs for book recommendations include more personalized recommendations, increased accuracy in predicting a user's preferences, and the ability to suggest books that may not have been previously considered but are similar to ones the user has enjoyed in the past.

4. Are there any limitations to using nearest neighbor graphs for book recommendations?

One limitation of using nearest neighbor graphs for book recommendations is that they rely heavily on user data and may not be as effective for new users with limited reading history. Additionally, they may not take into account other factors that influence a person's book preferences, such as genre, author, or writing style.

5. How can nearest neighbor graphs be improved for book recommendations?

To improve the effectiveness of nearest neighbor graphs for book recommendations, additional factors such as genre, author, and writing style can be incorporated into the algorithm. Collaborative filtering techniques can also be used to combine user data with data from other users with similar reading patterns. Regular updates and adjustments to the algorithm can also help improve its accuracy over time.

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