all. I'm totally new to Data Clustering and I was wondering if we want to perform Social Network Analysis or Visualization, what type of Clustering Technique is it based on?
There are like several categories of clustering methods such as Hierarchy-based, Density-based, Grid-based, etc. I'm not sure which one this Social Network Visualization falls into. Is it Grid-based? I did try googling but found none that answers :(
Thank you in advance!
Networks are graphs.
So you will need to use a graph clustering algorithm.
Related
I trying to produce a neural network visualization similar to the one below (link):
I was wondering if anybody could suggest any resources that they have used themselves and are happy with. If, in particular, anyone knows of any freely available template from which I can build off, that would be great.
I happened to e-mail the author who let me know that he used Powerpoint for this one. Who would have thought!
I have streaming continuous data that I need to filter by frequency. I have high-level knowledge of FFT and frequency filtering, but no real practical knowledge.
After searching for a suitable library to aid me, I came across Math.NET's Neodym Signal Processing Library (https://mathnetneodym.codeplex.com/releases/view/91769)
I downloaded it, upgraded it to VS2010 and used the SignalGenerator class to generate various sine wave of various frequencies. It worked great.
Now, I need to know how to filter dynamic data. This StackOverflow question seems to come the closest to mine, and is a good start:
Filtering continuous data, how to get rid of transients?
There is a .chm file in the downloaded project, but it seems to just be the API documentation. I need some high-level help just pointing me in the right direction to use the library.
Can anybody provide a good resource to refer to so I can use this library for my purpose?
Thank you!
John
UPDATE
I'm pretty sure I use the OnlineFilter.CreateBandpass static function with the mode ImpulseResponse.Infinite... but I'll post whatever I find out.
I am finding it increasingly difficult to find any specific material on the business applications of Neural Networks. This makes sense because many businesses try to keep their secrets to themselves, however, I have been wondering the following question: How do businesses use Neural Networks and what specific examples show their usage?
If anyone can provide some light on this matter, I would very much be appreciated.
Geodetic classification using neural networks attempts to infer useful information from multispectral satellite and other imaging. The results are used in agriculture, mining, civil engineering, simulation, etc. Some search results are seen here.
Thanks for reading.
I want to implement a Baysian Network using the Matlab's BNT toolbox.The thing is, I can't find "easy" examples, since it's the first time I have to deal with BN.
Can you propose some possible applications, (with not many nodes) please ^^ ?
Have a look at Tom Mitchell's "Machine Learning" book, which covers the subject starting with small, simple examples. I suspect there are many course slides you could access online which also give simple examples.
I think it helps to start with higher level tools to get a feel for how to construct networks before constructing them in code. Having a UI also allows you to play with the network and get a feel for the way the networks behave (propagation, explaining away, etc).
For example have a look at the free Genie (http://genie.sis.pitt.edu) and its samples, and/or the 50 node limited Hugin-Lite (http://www.hugin.com/productsservices/demo/hugin-lite) with it's sample networks. You can then check your BNT implementations to make sure they verify against the software packages.
Edit: I forgot to mention Netica which is another BN/Influence diagram software package which I think has the biggest selection of examples http://www.norsys.com/netlibrary/index.htm.
I'm trying to come up with the largest possible group of friends that would theoretically get along with each other, i.e., each person in the group should know at least 50% of the other people in the group.
I'm trying to come up with an algorithm for this that doesn't take ridiculously long; Facebook's API/cross-server talk is pretty slow as is.
I was thinking I could start with the friend that has the most mutual friends with me first, and then add people to the group one by one. But who would I choose next?
Just interested in the theory, no code is necessary.
Edit: When I said "theory", what I really meant what's the next logical step in plain english :) I was hoping I could code this up in an afternoon, but I guess this is a bit more complicated than I anticipated, and I'm not sure I want to spend weeks delving into heavy graph theory. Nevertheless, maybe someone else will find this interesting.
MIT did some work on social graphing a while back. Although it used mobile phone data, the clustering algorithms and other systems should still apply, even though they are constructed using different inputs and criteria.
There is more MIT chatter about social graphing going on at the moment. Definitely the place to look for technical pointers on this kind of thing.
Whilst the problem of graph enumeration from a given node to it's edges is NP complete for most useful problems ... the application of the graph traversal and the wealth of information might help you make this more efficient:
For any node (profile) N, you could data-scrape using Google or something to find associated edges out. This means that you can harness a cache of the pages and Googles search technology to mitigate having to traverse the edges yourself.
Social profiles contain tons of meta-data. Developing a statistical analysis method for working out the likelyhood of A knowing B without a direct path might be useful. Afterall friends have a) similar locations and b) similar interests
Other data, seemingly irrelevant can provide a means for locating people likely to know eachother and then you can double check the edges. Things such as chatter on boards about a band or gig, or people mentioning "cat fight" when Kate smacked Mary in the mouth.
The data just needs looking at in the right way, in the same way MIT looked at geographical statistics to determine relationships through phones.
Good Luck
There is an Algorithm called SCAN-Algorithm with some precalculations the algorithm can cluster a network in a good speed.
You can find informations about the algorithm here: SCAN: A Structural Clustering Algorithm for Networks
This is more "broad", but see if it helps to get ideas.