I have read the explanation in http://en.wikipedia.org/wiki/PageRank and i understand that the page rank is calculated by incoming links and out going links.
I have a crawler while crawls a webpage and store in db i need an page-rank algorithm.
I have a db with following values
Title
url
content_html
outgoing_links(external domain)
internal_links(the links with same domain of the url)
can u please explain do i need any other value to compute the page rank and. please explain how to compute it using java
PageRank is, at its heart, a linear algebra eigenvalue problem:
http://www.rose-hulman.edu/~bryan/googleFinalVersionFixed.pdf
If you don't know linear algebra or eigenvalue problems, or aren't willing to read this paper, it's unlikely that you'll be able to tackle this problem. As Einstein said, "Make the problem as simple as possible, but no simpler..."
The paper's title is old; it refers to Google's market cap circa 2004. It's up to $211B this morning.
The technology hasn't stood still in all that time. Google continues to tweak the algorithm in proprietary ways. But this paper explains the heart of it.
You have a few options. If you want to do it all yourself then duffymo's solution is perfect but if you want to use existing libraries I would suggest something similar to Jung for graphs.
I'm not sure if your familiar with graphs but they can be used to store the structure of the links and pagerank is often included in most libraries. Depending on how you want to do it, a good in memory solution is Jung but if you need persistent database storage than loading your data into Neo4J would work(you would have to install gremlin to do the pagerank).
The above are Java solutions but if you want to do it yourself(and like me don't like dry research papers) then I would highly suggest the book programming collective intelligence. They go through(chapter 4? I think) creating a search engine from scratch that includes pagerank and neural networks to monitor clicks. The only problem, based on your requirements above, is the book is written in python but you can easily apply the logic to java. If you know a bit of python already then you can even download the books source code for free and check out the software(but there is no explanation on the math behind the code in the source code).
Hope that helps
Related
in my project, I am using the library called LIUM_SpkDiarization-4.7.jar, but I am not quite sure how does it work. Could anyone, please, explain it a bit?
Also, I'm using it with python.
The link to the library is: https://voiceid.googlecode.com/svn-history/r11/trunk/scripts/LIUM_SpkDiarization-4.7.jar
Thanks in advance.
I was not aware of this tool. It looks really cool. Have you checked their wiki? They have some papers on how the system works: http://lium3.univ-lemans.fr/diarization/doku.php
Basically, they compute MFCC Mel Frequency Cepstrum Coefficients (standard technique). This is the fundamental step. It generates a feature space to work with. It is similar to computing FFT over sliding window in time. Ultimately clustering is performed on these time sliced features using Bayesian Information Criterion (BIC) methods. First to segment the time based feature space, then to cluster, and find consistent features for each speaker. HMM, viterbi, EM, and sometimes GMM can be used as well.
I don't know the algorithm well enough to explain it in detail, but this should also help: http://lium3.univ-lemans.fr/diarization/doku.php/overview
I am planing to write a Java application to identify certain types of trees using google maps . I have knoweldge in google maps api. my problem is how to recognize trees from map. I found some libs like jjil. But i don't know whether it is useful or not. Can any one give some inputs for this project ?
The general idea would probably be something like this:
Collect a large number of positive and negative samples (i.e. images from Google Maps that you want to recognize and ones that you don't want to recognize). It's hard to say how many you'll really need; depending on how similar they are and how many features you need, 100 might be enough or 10000 might be too few.
Find a set of texture features. Nobody can tell you what features are optimal without seeing the sample images (1) first.
Train a machine learning algorithm (e.g. SVM, neural network). Split the sample set into a training set and a test set to judge the discrimination quality.
Step 1 is just work, and I doubt anyone on Stackoverflow will do it for you. If you have the samples and post them, we might be able to help with steps 2/3, though.
I want to write an app to rename sort and organize my music library (mp3's, wav's, flac's). I wanted to take a portion of the song, say the first minutes, and compare that to a database and then retrieve the song name and tag information. I have heard that you can do this with last.fm but a look through their api info didn't help. My question is, what is this called so i can google it better? nothing I am trying is helping much. This would be similar to the shazam android app. My preferred language would be Java, so I can run it on a few operating systems easier, but that might be subject to change depending on how I can do it.
Okay I don't know if you need a practical or a technical answer.
Practically the best music database out there is MusicBrainz.
They have developed a fingerprinting technology that calculats what they I think call PUIDs.
The database is Huge (its the biggest out there), and there are tools available. And its free.
Picard Tagger is a cross platform tool for exactly what you are trying to do.
Technically there are a lot of different approaches. Especially in the Audio segment there are really a lot of methods. Most rely on frequency spectrum analysis. But also take into account Rythm, and developement of certain characteristics over time and of course trivial parameters like length etc.
Searching for audio fingerprinting should give you a lot of results.
Have a look at this paper which details how the Shazam algorithm works.
I highly recommend the Echo Nest API for this sort of task. Their clientele are exactly app builders like you. It has a large database, is easy to use, and can retrieve the song information you want.
did you consider atunes?
the source code is at source forge . can identify the song, written in Java.
good luck.
Here is a fairly easy to understand article about this:
http://www.soyoucode.com/2011/how-does-shazam-recognize-song
I'm planning on building a feature for an e-commerce platform I developed in Java to display related products in much the same way Amazon does. There are a few different metrics for relating products that I want to explore.
Purchase history (purchased at the same time)
Related by family/type (similar product classifications)
Intentionally related (boosting results; "Buy this!")
While I would probably be able to develop my own datamining library, it wouldn't be very portable and I dare say it wouldn't be very good either.
There are several packages out there for doing this sort of thing but I don't feel like I am in a position to evaluate which package or solution would work best for me. Any input anecdotal or from personal experience would be greatly appreciated.
Note: I've tagged this as Neural networking because of a python talk I was at where a neural-like-network was used for datamining, I'm not convinced a neural network is the best choice for this job.
Take a look at Apache Mahout
There are some artificial algorithm techniques used for data mining, such as C4.5 or ID3. These algorithm does classification. Other techniques such as ant clustering, neural networks or genetic algorithms are used for classification purposes in data mining.
As far as algorithms, I don't know much but ID3/C4.5 can be easily programmed.
Hope this helps.
I'm planning to develop program in Java which will provide diagnosis. The data set is divided into two parts one for training and the other for testing. My program should learn to classify from the training data (BTW which contain answer for 30 questions each in new column, each record in new line the last column will be diagnosis 0 or 1, in the testing part of data diagnosis column will be empty - data set contain about 1000 records) and then make predictions in testing part of data :/
I've never done anything similar so I'll appreciate any advice or information about solution to similar problem.
I was thinking about Java Machine Learning Library or Java Data Mining Package but I'm not sure if it's right direction... ? and I'm still not sure how to tackle this challenge...
Please advise.
All the best!
I strongly recommend you use Weka for your task
Its a collection of machine learning algorithms with a user friendly front-end which facilitates a lot of different kinds of feature and model selection strategies
You can do a lot of really complicated stuff using this without really having to do any coding or math
The makers have also published a pretty good textbook that explains the practical aspects of data mining
Once you get the hang of it, you could use its API to integrate any of its classifiers into your own java programs
Hi As Gann Bierner said, this is a classification problem. The best classification algorithm for your needs I know of is, Ross Quinlan algorithm. It's conceptually very easy to understand.
For off-the-shelf implementations of the classification algorithms, the best bet is Weka. http://www.cs.waikato.ac.nz/ml/weka/. I have studied Weka but not used, as I discovered it a little too late.
I used a much simpler implementation called JadTi. It works pretty good for smaller data sets such as yours. I have used it quite a bit, so can confidently tell so. JadTi can be found at:
http://www.run.montefiore.ulg.ac.be/~francois/software/jaDTi/
Having said all that, your challenge will be building a usable interface over web. To do so, the dataset will be of limited use. The data set basically works on the premise that you have the training set already, and you feed the new test dataset in one step, and you get the answer(s) immediately.
But my application, probably yours also, was a step by step user discovery, with features to go back and forth on the decision tree nodes.
To build such an application, I created a PMML document from my training set, and built a Java Engine that traverses each node of the tree asking the user to give an input (text/radio/list) and use the values as inputs to the next possible node predicate.
The PMML standard can be found here: http://www.dmg.org/ Here you need the TreeModel only. NetBeans XML Plugin is a good schema-aware editor for PMML authoring. Altova XML can do a better job, but costs $$.
It is also possible to use an RDBMS to store your dataset and create the PMML automagically! I have not tried that.
Good luck with your project, please feel free to let me know if you need further inputs.
There are various algorithms that fall into the category of "machine learning", and which is right for your situation depends on the type of data you're dealing with.
If your data essentially consists of mappings of a set of questions to a set of diagnoses each of which can be yes/no, then I think methods that could potentially work include neural networks and methods for automatically building a decision tree based on the test data.
I'd have a look at some of the standard texts such as Russel & Norvig ("Artificial Intelligence: A Modern Approach") and other introductions to AI/machine learning and see if you can easily adapt the algorithms they mention to your particular data. See also O'Reilly, "Programming Collective Intelligence" for some sample Python code of one or two algorithms that might be adaptable to your case.
If you can read Spanish, the Mexican publishing house Alfaomega have also published various good AI-related introductions in recent years.
This is a classification problem, not really data mining. The general approach is to extract features from each data instance and let the classification algorithm learn a model from the features and the outcome (which for you is 0 or 1). Presumably each of your 30 questions would be its own feature.
There are many classification techniques you can use. Support vector machines is popular as is maximum entropy. I haven't used the Java Machine Learning library, but at a glance I don't see either of these. The OpenNLP project has a maximum entropy implementation. LibSVM has a support vector machine implementation. You'll almost certainly have to modify your data to something that the library can understand.
Good luck!
Update: I agree with the other commenter that Russel and Norvig is a great AI book which discusses some of this. Bishop's "Pattern Recognition and Machine Learning" discusses classification issues in depth if you're interested in the down and dirty details.
Your task is classical for neural networks, which are intended first of all to solve exactly classification tasks. Neural network has rather simple realization in any language, and it is the "mainstream" of "machine learning", closer to AI than anything other.
You just implement (or get existing implementation) standart neural network, for example multilayered network with learning by error back propagation, and give it learning examples in cycle. After some time of such learning you will get it working on real examples.
You can read more about neural networks starting from here:
http://en.wikipedia.org/wiki/Neural_network
http://en.wikipedia.org/wiki/Artificial_neural_network
Also you can get links to many ready implementations here:
http://en.wikipedia.org/wiki/Neural_network_software