#sdmax

Identifying the top 10 algorithms in the abstract is a pretty complicated exercise unless there is a clear dimension to make the comparison. Popularity? Usefulness? Research merit? Let me tackle this from a pretty subjective point of view: If I were interviewing you for a Data Mining position what would be the top 10 algorithms I would expect you to know in order of priority?

Linear regression

Logistic regression

k-means

SVMs

Random Forests

Matrix Factorization/SVD

Gradient Boosted Decision Trees/Machines

Naive Bayes

Artificial Neural Networks

For the last one I'd let you pick one of the following:

Bayesian Networks

Elastic Nets

Any other clustering algo besides k-means

LDA

Conditional Random Fields

HDPs or other Bayesian non-parametric model

Again, a pretty subjective list, but I think it is quite representative of what you need to do real data mining work in industry.

...one? It was in second grade, barely even counted as a fist fight (I don’t think any physical contact was actually made), and I had to write an apology letter after the fact, which probably put me off doing anything else like that.