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Poll |
| Which data mining techniques do you use regularly?
(Choose several) [212 votes, 825 choices] |
| Decision Trees/Rules (128) |
16% |
| Clustering (103) |
12% |
| Statistics (101) |
12% |
| Logistic regression (75) |
9% |
| Neural networks (75) |
9% |
| Association rules (63) |
8% |
| Visualization (52) |
6% |
| Nearest neighbor (42) |
5% |
| Text mining (30) |
4% |
| Sequence analysis (27) |
3% |
| Genetic algorithms (26) |
3% |
| Bayesian nets (24) |
3% |
| Hybrid methods (21) |
3% |
| Naive Bayes (19) |
2% |
| Web mining (19) |
2% |
| Other (20) |
2% |
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Comments
J.P.Brown, SuperInduction
The list of
techniques that people could be using was wide-ranging, but the list
that I am offering includes some others.
Keeping it simple, my list includes:
Statistics; Probability Scale; Classification; Neural Nets &
Clementine
Bruno Delahaye, Others: Techniques using SRM
Vapnik's theory 'Structured Risk
Minimisation' (SRM) allows to transform traditionnal algorithms into
robust algorithms. For instance KXEN has implemented "Robust
Regression" (for regression and classification) i.e SRM applied to
polynomial functions.
Krzysztof Cios, hybrid methods
hybrid methods are, for example, methods
which are a combination of rule machine learning algorithms and
decision tree learning algorithms (like CLIP3 or CN2)
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