Top data mining algorithms in plain English

http://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/

by Rexxar • 11 years ago

318 22 11 years ago

rayli.net

leeleelee• 11 years ago

Very, very awesome article.

stellographer2• 11 years ago

I found this article useful.

What I was really hoping for was a layman's translation of the maths on wikipedia [ie. how to implement]. But this is a good jumping off point for figuring out which black box to use.

blumkvist• 11 years ago

So you want to do math without knowing how to do math?

stellographer2• 11 years ago

I'm just saying it takes about 30 seconds to explain the Metropolis-Hastings algorithm in plain english, and the wikipedia article is almost intentionally esoteric on the matter:

http://en.wikipedia.org/wiki/Metropolis–Hastings_algorithm

Meanwhile the much more complex Firefly algorithm is adequately explained in just 3 sentences that are obvious to understand:

http://en.wikipedia.org/wiki/Firefly_algorithm

profinger• 11 years ago

This is what I was thinking as well! The explanation is good but it's the actual steps of the algorithm that are the difficult part!

zatkin• 11 years ago

Wow, this is an awesome read. I especially like how each algorithm starts with a simple explanation and then dives deeper into each lesser known vocabulary.

kiril-me• 11 years ago

You can significantly improve C4.5 speed using See5/C5.0

lessthunk• 11 years ago

Great to get specialist knowledge/understanding into the mainstream.

tomelders• 11 years ago

I'll add my noise to the cacophony and agree that this is a grat article. I've been struggling with an idea for a while, and this has opened up a whole new horizon of possibility for me. So thanks.

aaronsnoswell• 11 years ago

What an excellent article!

Grue3• 11 years ago

How convenient, I'm currently tasked with classifying some data and this will surely come in handy.

mrfusion• 11 years ago

I'd be interested to see some computer vision algorithms explained this way too, SIFT, SURF?

zild3d• 11 years ago

I'd be interested in just about every algorithm explained this way! And as a side note some of these algorithms certainly are used in computer vision, k-means for example in clustering/segmentation.

https://courses.cs.washington.edu/courses/cse576/12sp/notes/...

honodk• 11 years ago

Have a look at this: https://gilscvblog.wordpress.com/2013/08/18/a-short-introduc... . I found it very helpful!

aerovistae• 11 years ago

Can we have more things like this, please? This is an AMAZING introduction to these concepts.

I wish more people on Hacker News would make such clear blog posts for their personal share of cryptic knowledge, helping other people through the door.

bipin_nag• 11 years ago

I also want that, the side-by-side comparison specially. The follow up discussions are also helpful, someone mentioned about C5.0 I didn't know about. There are lot of algorithms/steps pieces that are needed when solving the machine learning puzzle. The article was very helpful.

ma2rten• 11 years ago

I feel like some of these algorithms are outdated, i.e. rarely used anymore: C4.5, Apriori, CART.

Instead I would suggest: Logistic Regression, RandomForest and Neural Networks.

shazeline• 11 years ago

CART forms the basis of the decision trees used by scikit-learn's implementation of random forests [1]. CART may be old, but definitely not outdated.

[1] https://github.com/scikit-learn/scikit-learn/blob/master/skl...

natch2• 11 years ago

Awesome. What is a 'kernel' btw?

Bostonian• 11 years ago

In kernel density estimation, each observed data point is spread out by a "kernel" function such as a Gaussian, essentially exp(-0.5*x^2), or uniform function, f(x) = 1 for |x| < 0.5. So I think of a "kernel" as a function used to distribute a point mass.

natch2• 11 years ago

In English?

diego_moita• 11 years ago

Wow! It explains, in 20-30 minutes, what took me a whole semester to study at the University.

I only wish it had a little about Neural Networks and Deep Learning.

Top data mining algorithms in plain English

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