This was the subject of a question asked on Quora: What are the top 10 data mining or machine learning algorithms?
Some modern algorithms such as collaborative filtering, recommendation engine, segmentation, or attribution modeling, are missing from the lists below. Algorithms from graph theory (to find the shortest path in a graph, or to detect connected components), from operations research (the simplex, to optimize the supply chain), or from time series, are not listed either. And I could not find MCM (Markov Chain Monte Carlo) and related algorithms used to process hierarchical, spatio-temporal and other Bayesian models. What else in missing?
In 2006, the IEEE Conference on Data Mining identified the top 10 ML algorithms as
An answer to the Quora question, in 2011, lists the following as potential candidates or additions:
And a 2015 answer provides the following:
My point of view is of course biased, but I would like to also add some algorithms developed or re-developed at the Data Science Central's research lab:
These algorithms are described in the article What you wont learn in statistics classes.
Regarding the Indexation algorithms (see Part 2 after clicking on this link): This must be at least 20 years old. It is an incredibly fast clustering technique indeed: it does not require n x n memory storage, only n, where n is the number of observations. Also, it is easy to implement in distributed Map-Reduce or Hadoop environments. It is a fundamental algorithm: the core algorithm used to build taxonomies, catalogs (see this article about Amazon), search engines, and enterprise search solutions. DSC used it successfully in numerous contexts including for IoT automated growth hacking for digital publishing, to categorize articles and boost them depending (among other things) on category, for maximum efficiency. Here's another illustration.