Unfortunately, there is no simple answer, because the differing characteristics of different algorithms render them more or less suitable for different problems. While within in the data mining community there is much literature, for example, comparing different algorithms on a specific dataset, or looking at their theoretical properties in idealized (read: unrealistic) situations, there is much less available to help make a practical choice with real data. We attempt to remedy that here by comparing and contrasting the characteristics of some commonly used algorithms.

Given the number of factors influencing the decision, we do not recommend a particular algorithm. However, if it is truly unknown where to start, the decision tree is a good bet, because, although it may not provide the best results, it is generally robust to issues encountered in real-world data.

An interesting point to note is that, aside from their relative popularity, none of the attributes of the algorithms mentioned are specific to astronomy, or, indeed, any given problem. This emphasizes their generic utility for a variety of analyses, and hence, science goals.

Material for this table is based on section 4 of this guide, Table 1 of Ball & Brunner (2010), and Table 10.1 of Hastie et al. (2001).

Algorithm | Advantages | Disadvantages |
---|---|---|

Artificial Neural Network | Good approximation of nonlinear functions | Black-box model |

Easily parallelized | Susceptible to local minima | |

Good predictive power | Non-trivial architecture: many adjustable parameters | |

Extensively used in astronomy | Sensitive to noise | |

Robust to irrelevant or redundant attributes | Can overfit | |

Training can be slow | ||

No missing values | ||

Decision Tree | Popular real-world data mining algorithm | Can generate large trees that require pruning |

Can input and output numerical or categorical variables | Generally poorer predictive power than ANN, SVM or kNN | |

Interpretable model, easily converted to rules | Can overfit | |

Robust to outliers, noisy or redundant attributes, missing values | Many adjustable parameters | |

Good computational scalability | Building the tree can be slow (data sorting) | |

Genetic algorithm | Able to avoid getting trapped in local minima | Not guaranteed to find the local minimum |

Easily parallelized | Non-trivial choice of fitness function and solution representation | |

Can be slow to converge | ||

Support Vector Machine | Copes with noise | Harder to classify > 2 classes |

Gives expected error rate | No model is created | |

Good predictive power | Long training time | |

Popular algorithm in astronomy | Poor interpretability | |

Can approximate nonlinear functions | Poor at handling missing or irrelevant attributes | |

Good scalability to high-dimensional data | Can overfit | |

Gives unique solution (no local minima) | Some adjustable parameters | |

k-Nearest Neighbor | Uses all available information | Computationally intensive (but can be mitigated, e.g., k-d tree) |

Does not require training | No model is created | |

Easily parallelized | Susceptible to noise and irrelevant attributes | |

Few or no adjustable parameters | Performs poorly with high-dimensional data | |

Good predictive power | ||

Uses numerical data | ||

Conceptually simple, and easy to code | ||

Good at handling missing values and outliers | ||

k-means clustering | Well-known, simple, popular algorithm | Susceptible to noise |

Many extensions, e.g., | Biased towards finding spherical clusters | |

probabilistic cluster membership | Has difficulties with different densities in the clusters | |

constrained k-means, guided by training data | Affected by outliers | |

Requires numerical inputs if using Euclidean distance | ||

Not guaranteed to find local minimum | ||

Basic algorithm requires no overlap between classes | ||

Mixture Models & Expectation Maximization | Gives number of clusters in the data | Can be biased toward Gaussians |

Suitable for clusters of differerent density, size and shape | Local minima | |

Copes with missing data | Doesn't work well with a large number of components | |

Can give class labels for semi-supervised learning | Can be slow to converge | |

Requires numerical data, not categorical | ||

Kohonen Self-Organizing maps | Well-known, popular algorithm | Susceptible to noise |

Requires normalized data | ||

Susceptible to outliers | ||

Often requires a large number of iterations through the data | ||

Decompositions (SVD, PCA, ICA) | PCA is extensively used in astronomy | Applicable to numerical data only |

Can be extended to the nonlinear case and noisy data | Requires whitening of the data as preprocessing step | |

Sensitive to outliers if maximum likelihood is used |

- Ball N.M. & Brunner R.J., "Data Mining and Machine Learning in Astronomy", International Journal of Modern Physics D 19 (7) 1049-1106 (2010); arXiv/0906.2173
- Hastie T., Tibshirani R. & Friedman J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, 1st edn. (Springer, New York, 2001).

-- NickBall - 05 Sep 2010

-- NickBall - 23 Sep 2011

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