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

1. C4.5 (Decision Trees)
2. k-Means (clustering)
3. Support Vector Machines (SVM)
4. Apriori
5. Expectation Maximization (EM)
6. PageRank
7. AdaBoost
8. k-Nearest Neighbors (kNN)
9. Naive Bayes
10. Classification and Regression Tree (CART)

An answer to the Quora question, in 2011, lists the following as potential candidates or additions:

1. Kernel Density Estimation and Non-parametric Bayes Classifier
2. K-Means
3. Kernel Principal Components Analysis
4. Linear Regression
5. Neighbors (Nearest, Farthest, Range, k, Classification)
6. Non-Negative Matrix Factorization
7. Support Vector Machines
8. Dimensionality Reduction
9. Fast Singular Value Decomposition
10. Decision Tree
11. Bootstapped SVM
12. Decision Tree
13. Gaussian Processes
14. Logistic Regression
15. Logit Boost
16. Model Tree
17. Naïve Bayes
18. Nearest Neighbors
19. PLS
20. Random Forest
21. Ridge Regression
22. Support Vector Machine
23. Classification: logistic regression, naïve bayes, SVM, decision tree
24. Regression: multiple regression, SVM
25. Attribute importance: MDL
26. Anomaly detection: one-class SVM
27. Clustering: k-means, orthogonal partitioning
28. Association: A Priori
29. Feature extraction: NNMF

And a 2015 answer provides the following:

1. Linear regression
2. Logistic regression
3. k-means
4. SVMs
5. Random Forests
6. Matrix Factorization/SVD
7. Gradient Boosted Decision Trees/Machines
8. Naive Bayes
9. Artificial Neural Networks
10. For the last one I'd let you pick one of the following:
11. Bayesian Networks
12. Elastic Nets
13. Any other clustering algo besides k-means
14. LDA
15. Conditional Random Fields
16. HDPs or other Bayesian non-parametric model

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:

• Jackknife regression
• Feature extraction / selection (mentioned above, but this version is very different)
• Hidden decision trees
• Indexation and tagging algorithms

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. 

DSC Resources

• Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
• Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC
• Buzz: Business News | Announcements | Events | RSS Feeds
• Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers

Additional Reading

• The 10 Best Books to Read Now on IoT
• 50 Articles about Hadoop and Related Topics
• 10 Modern Statistical Concepts Discovered by Data Scientists
• Top data science keywords on DSC
• 4 easy steps to becoming a data scientist
• 13 New Trends in Big Data and Data Science
• 22 tips for better data science
• Data Science Compared to 16 Analytic Disciplines
• How to detect spurious correlations, and how to find the real ones
• 17 short tutorials all data scientists should read (and practice)
• 10 types of data scientists
• 66 job interview questions for data scientists
• High versus low-level data science

Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge