Abstract: Principal Component Analysis (PCA) aims to acquire the principal component space containing the essential structure of data, instead of being used for mining and extracting the essential ...

Part II: Unsupervised machine learning in R to cluster and identify candidate countries for international expansion, using PCA, K-Means, and DBSCAN.

Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

Unlike PCA (maximum variance) or ICA (maximum independence), ForeCA finds components that are maximally forecastable. This makes it ideal for time series analysis where prediction is often the primary ...

With the increasing complexity of analytical data nowadays, great reliance on statistical and chemometric software is quite common for scientists. Powerful open-source software, such as Python, R, and ...

We describe OHBA Software Library for the analysis of electrophysiology data (osl-ephys). This toolbox builds on top of the widely used MNE-Python package and provides unique analysis tools for ...

ABSTRACT: This article examines the effect of economic vulnerability on inclusive growth across 49 developing countries from 1991 to 2020, focusing on the mitigating role of agricultural structural ...

ABSTRACT: This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on ...