| Package | Description |
|---|---|
| smile.association |
Frequent item set mining and association rule mining.
|
| smile.classification |
Classification algorithms.
|
| smile.clustering |
Clustering analysis.
|
| smile.clustering.linkage |
Cluster dissimilarity measures.
|
| smile.feature |
Feature generation, normalization and selection.
|
| smile.gap |
Genetic algorithm and programming.
|
| smile.imputation |
Missing value imputation.
|
| smile.manifold |
Manifold learning finds a low-dimensional basis for describing
high-dimensional data.
|
| smile.mds |
Multidimensional scaling.
|
| smile.neighbor |
Nearest neighbor search.
|
| smile.projection |
Feature extraction.
|
| smile.regression |
Regression analysis.
|
| smile.sampling |
Sampling is concerned with the selection of a subset of individuals
from within a statistical population to estimate characteristics of
the whole population.
|
| smile.sequence |
Learning algorithms for sequence data.
|
| smile.taxonomy |
A taxonomy is a tree of terms (concepts) where leaves
must be named but intermediary nodes can be anonymous.
|
| smile.util |
Utilitiy functions used in many places and multicore executor.
|
| smile.validation |
Model validation.
|
| smile.vq |
Originally used for data compression, Vector quantization (VQ)
allows the modeling of probability density functions by
the distribution of prototype vectors.
|
| smile.wavelet |
Discrete wavelet transform (DWT).
|