# Datamining

The DataMining package in this repository provides tools and algorithms for extracting valuable insights from large datasets. It includes functionalities for data preprocessing, clustering, classification, and visualization, making it a comprehensive solution for data analysis tasks.

For more detailed information, refer to the specific class implementations and their docstrings.

## Sampling Models

### LHS

The [`Latin Hypercube Sampling (LHS)`](sampling_datamining.md) model is used for generating a distribution of plausible collections of parameter values from a multidimensional distribution. It ensures that the entire range of each parameter is explored by dividing the range into intervals of equal probability and sampling from each interval.

## Clustering Models

### MDA

The [`Maximum Dissimilarity Algorithm (MDA)`](clustering_datamining.md) model is a sampling technique used to select a subset of data points that are maximally dissimilar from each other, ensuring a diverse representation of the dataset.

### KMA

The [`K-Means Algorithm (KMA)`](clustering_datamining.md) model is a clustering method that partitions the dataset into K distinct, non-overlapping subsets.

### SOM

The [`Self-Organizing Map (SOM)`](clustering_datamining.md) model is a type of artificial neural network used for unsupervised learning to produce a low-dimensional representation of the input space.

## Reduction Models

### PCA

The [`Principal Component Analysis (PCA)`](reduction_datamining.md) model is a dimensionality reduction technique that transforms the data into a set of orthogonal components, capturing the most variance.
