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Anybody used Neural Network approaches for clustering data? Particularly
ART Neural Network (Adaptive Resonance Theory) or
Kohonen self organizing maps
How are they as compared to k-means or any other distance based clustering Algorithms?
Self-organizing maps (SOMs) have some internal similarities with K-Means, but also important differences. A SOM actually maps your data from the original data space (usually high-dimensional) onto the map space (usually two-dimensional), while trying to preserve the original data densities and neighborhood relationships. It won't give you directly the clustering, but may help you to visually inspect the data and recognize clusters.
I know too little about ART nets.
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I have heard a lot about database sharding and cluster. But I am not able to fully understand these concepts. If I have a lot of data, how sharding and clustering can solve it? Are sharding and clustering same or different?
Sharding is to spread the data across several databases with a way to access them that does not have to explicitly refer to the physical location.
Clustering usually means to establish a tight bond between several machines, so that services can run on either of the machines and be relocated to a different machine in case one machine has a problem.
Your problem lies in your question: how sharding and clustering can solve it.
Concentrate on that “it” and figure out what exactly you need to do with these data. Then you will be able to find the proper solution more easily.
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I have few doubts on database storage techniques:
How to store CPU usage activity to consider it for later use?
How to store RAM usage variation for a certain amount of time?
Similarly, how to store Disk usage?
All these data will be later used for ANOVA test.
I am trying to get these values from a c# application which will be monitoring the activities of a system for a certain amount of time.
A much better idea is to use the Performance Manager built into Windows (perfmon.exe). You can set it to record many performance items including the three you mention (CPU and RAM by program as well as in total). There is also a free analyser called PAL at Codeplex which can help you set the recording and then analyse it for you.
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How I can come up with an effective BodyBuilding database, what entities are most important?
From which I should be able to come up with an ER diagram. I'm still a novice to databases, but I have read quite enough, most difficult I'm finding a problem with differentiating between a Gym database and BodyBuilding Club database.
Have you tried listing everything you think relates to the database, then removing the ones you don't like, then grouping them in a way that makes sense to you or users of the database?
Certification types, access to bodybuilding books, bodybuilding trainers, ease of super sets, free weight ratios, ratings from heavy lifters, etc.
Try everything, make a list, narrow it down to the items needed for proper function and your business perspective (safety, legal, etc).
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I've been searching about this on google, but I get a lot of external information like: it is self healing and more stable etc etc..
Can someone explain the internal workings of the two systems, in terms of how they store data, retrieve them and any other differences between them. I gather FAT stores in clusters of bytes and has a "FAT" table to store entries. Also they mention that each cluster has a link to the next. How is FAT working exactly when I poll for a particular file. What happens in NTFS?
Your question is extremely general - so just some links with information about NTFS:
http://msdn.microsoft.com/en-us/library/ms995846.aspx
http://www.ntfs.com/ntfs_vs_fat.htm
http://www.ntfs.com/ntfs_basics.htm
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In developing a face recognition we first need to detect faces.Recent way is to train a system on known databases i.e artificial intelligence and neural networks.I would like to know how this training is done?
When training your system, you will need to train a classifier to distinguish between faces and non-faces relying on a set of features.
These features can be defined differently, but for a simple approach, the whole array of pixels can be used as features. One pixel = one feature.
Then you would need to apply a training algorithm. Common ones are logistic regression and support vector machines.
The training consists in correctly weighing the individual features to obtain the correct classification.
This can be done by minimizing a cost function.
You will need to separate your examples into at least two sets - the training set and the test set, to validate the results you get from training on a test set.