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) 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) 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) model is a clustering method that partitions the dataset into K distinct, non-overlapping subsets.

SOM

The Self-Organizing Map (SOM) 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) model is a dimensionality reduction technique that transforms the data into a set of orthogonal components, capturing the most variance.