Top ten algorithms in data mining (2007) [pdf]

http://some-docs.googlecode.com/files/Top%2010%20algorithms%20in%20data%20mining.pdf

by daoudc • 14 years ago

218 41 14 years ago

some-docs.googlecode.com

larrydag• 14 years ago

Oldie paper but still a goodie. Some other algorithms to consider... Random Forests, Gradient Boosted Machines (similar to AdaBoost), Logistic Regression, Neurel Networks.

tocomment4• 14 years ago

What's so good about random forests? I seem to be hearing it mentioned a lot lately.

mturmon• 14 years ago

Crudely: combine the speed of classification trees with the generally low error rates (flexibility) of the SVM.

_delirium2• 14 years ago

They were one of the first widespread ensemble methods (based on bagged decision trees), and share with other bagging/boosting methods fairly good empirical performance on a wide range of problems.

nwenzel• 14 years ago

Random Forests are interesting because they are designed to make many, many bad decisions that cancel each other out leaving only the good decisions behind.

To oversimplify, each of the many, many decision trees is going to do a poor job building a classifier. Each tree only uses a subset of the data and a subset of the features. So, the results of each tree won't generalize well (and may not even do well classifying the data it was trained on). But, for each tree that misclassifies in one direction, another tree will misclassify in the other direction. The theory (which appears to be validated in practice) is that poor decisions cancel each other out but good decisions don't get canceled out.

Although, it isn't quite a simple as a "good" tree and a "bad" tree. All decision trees are bad but there should randomly be good parts in the bad trees. Each tree is going to be "mostly crap" (as Jeremy Howard puts it). But "mostly crap" means "a little good." The crap in one tree cancels out the crap in another tree. The good parts are left behind... and... like magic... you have a good classifier that is fast although somewhat of a black box.

Jeremy Howard from Kaggle is a big believer and where most of my knowledge of Decision Tree Ensemble algorithms come from: https://www.kaggle.com/wiki/RandomForests

msellout• 14 years ago

A random forest builds a diversified portfolio of decision trees.

To the extent that the errors of the individual trees are uncorrelated, the random forest keeps the same low expected error of a single tree while reducing the high error variance of a tree. This is the same reason a mutual fund is better than a single stock -- on average they have the same return, but the latter has far more variance.

phatbyte• 14 years ago

As a self-taught programmer I always had a hard time reading descriptive algorithms. Any suggestions on how to learn reading it ?

sk55• 14 years ago

Here is a concise PDF of definitions and notation https://docs.google.com/open?id=0B_WU5GXujPOVRDFYRUlKcFF4RWs

It's from CMU's course titled "Concepts of Math" The professor, Brendan Sullivan, just wrote the textbook in an easy to understand style.Here's a link to the course: https://colormygraph.ddt.cs.cmu.edu/21127-f12/ and if you click the AnnotateMyPDF website, you can get a copy of the textbook, practice problems, etc for a more in depth understanding.

mikevm• 14 years ago

I clicked the AnnotateMyPDF, but it looks like you need to be signed up for the course to see those materials :(

bosie• 14 years ago

I just signed up for the AnnotateMyPDF website but i see nothing regarding the course in there. is it only available to CMU students?

svachalek• 14 years ago

You could try Knuth's Art of Computer Programming, Volume 1. It's in a formal style but covers some simpler algorithms and it's by an incredible writer. It's painstaking to read but some find it to be nearly a religious experience.

glogla3• 14 years ago

Visualisations are very powerful thing for understanding algorithms. For the classical CS undergrad ones (DFS, various shortest path or minimal spanning tree ones, and of course all of the sorting), visualisations are easy enough to find on youtube, but with less well known algorithms, sometimes you have to draw one yourself.

Another thing I sometimes do is run a few iterations of the algorithm myself on paper.

cllns• 14 years ago

Hm, the scribd link is prompting me to download the pdf

glogla3• 14 years ago

I would expect GUHA instad of Apriori, but I guess Apriori is more influential. But it's not like either of them is new.

caycep• 14 years ago

did they datamine a paper on data mining?

11001• 14 years ago

(2007)

tocomment4• 14 years ago

We does he say K-means isn't great? It always seemed like it would be useful if you have lots of data.

You're kind of looking for the most similar records to match each record to. Am I understanding that right?

paulgb• 14 years ago

The groups are dependent on the initial configuration. Have a look at the (poor) grouping on page 8.

taeric• 14 years ago

The paper enumerates all of the shortcomings it has. So, what more did you need? Essentially, it can be highly influenced by where the initial means are chosen and the existence of outliers. Further, on multidimensional data you have to take care in choosing the distance formula. (There was a great blog not long ago about using K-Means to find the "palette" of an image where many of these shortcomings can be seen easily.)

That isn't to say the algorithm can not be used. It just is not bullet proof.

kikas• 14 years ago

All the presented algorithms have pros and cons. You have to understand the algorithms(tools) at your disposal and find the best for the questions you want to make.

In the K-means case, it is good to know beforehand how many groups(clusters) you are expecting. Otherwise, there are other clustering algorithms(like gdbscan) which detect the number of cluster automatically(although you have to provide the average expected density). But once again all depends on the question you want to answer, or if you are just cleaning the data a bit.

Know your data, know your algorithms and always question the results.

canthonytucci• 14 years ago

I had the pleasure of studying under Prof Xindong Wu at UVM as an undergraduate and have some slides from, and maybe even a video of a talk he gave to the UVM Computer Science Student Association. If there's interest I'd be happy to track them down.

mooneater• 14 years ago

Missing Logistic Regression: so old school (from the 1930s), so mathematically rigorous (compare to neural networks for example), so powerful, and so often overlooked.

tocomment4• 14 years ago

Can you explain more about why you like it?

yid• 14 years ago

Neural networks are well understood as additive models. They may not have nice closed form training algorithms, but they are also more expressive than logistic regression.

textminer3• 14 years ago

Learning about the basis of logistic regression in Ng's Stanford class was eye-opening. Also like that he then motivated generalized linear models, and why they're nice (e.g., parameters are linear with input data; the maximum-likelihood hypothesis is the expectation of the sufficient statistic) and he explains why we see the logistic function in so many damn places (it's the response function whenever y|x is distributed as an exponential family).

alook• 14 years ago

100% agree.

It was great how he spent a lot of time on logistic regression before delving into SVM's or Neural Nets - it was much easier to understand the cost functions & regularization for other types of classifiers after having understood those for logistic regression.

My takeaway: if you can avoid adding risk to your systems by using more complicated models, you should.

dxbydt2• 14 years ago

In the textbook "multivariate stats" by izenman, (http://www.amazon.com/gp/product/0387781889/ ) , he claims that stats & ML progressed in parallel. So traditional stats techniques like OLS, multiple regression, nonlinear regression, logistic regression, GLMs are generally not covered in ML. Similarly ML topics like k-means, svm, random forests etc. are not taught by the stats dept.

What is happening in this past decade is a convergence of stats & ML, primarily driven by data scientists working in the domain of big data. The stats folks are slowly incorporating ML techniques into stats & finding rigorous heuristics for when they should be employed. Similarly ML guys, who are mostly CS folk who unfortunately have taken only 1 course on undergraduate stats & probability, are discovering you can do so much more without resorting to needless large-scale computation, by sampling intelligently & leveraging plain old statistics.

This schism between stats & ML can be leveraged very profitably during interviews :))

When I interview data science folks, I usually ask very simple questions from plain stats - how would you tell if a distribution is skewed...if you have an rv X with mean mu, and say rv Y = X-mu, then what is the mean of Y...if you have an rv A with mean 0 variance 1, then what are the chances of being 3 standard deviations away from the mean if you have no clue about the distribution of A ? What if you knew A was unimodal ? What if A is now normally distributed ?

Now if its a stats guy, I ask very simple ML....what is perceptron, have you heard of an neural network etc.

surprisingly, the stats guys do much better on ML than the ML guys on stats!

malkarouri• 14 years ago

Not surprising, really. ML is the shiny new thing, so the MLers don't tend to feel they missed anything while the statisticians need to keep up with the times.

I say this as an MLer still struggling to find out what R^2 is, among other things ..

fiter• 14 years ago

In the paper they mention logistic regression as an extension of the naive Bayes model (see page 26).

"Another extension to the naive Bayes model was developed entirely independently of it. This is the logistic regression model."

psb217• 14 years ago

Conceptually, logistic regression is a simple neural network. A neural network with no hidden layers and a softmax output layer using cross-entropy loss is precisely equivalent to logistic regression. Though, if you were to extend your suggestion of logistic regression to include its locally-weighted variants, I'd gladly support your cause.

sgt1012• 14 years ago

As Fisher said, the task is to separate the noise from the data!

lkozma• 14 years ago

Four years ago I took a class based on that paper where we implemented all ten algorithms (every participant every algorithm). It was a very instructive and somewhat painful experience. The course website is still online, if anyone is interested, the presentations of the algorithms and the matlab/python code stub for each algorithm might be useful.

http://www.cis.hut.fi/Opinnot/T-61.6020/2008/

Top ten algorithms in data mining (2007) [pdf]

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