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 ...

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 ...

Department of Psychology, SVKM’s Mithibai College of Arts, Chauhan Institute of Science and Amrutben Jivanlal College of Commerce and Economics (Empowered Autonomous), Mumbai, India Introduction: ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No ...

Amid the wave of the digital age, advanced technologies such as big data, artificial intelligence, and cloud computing are driving precise analysis and forecasting across various fields. This paper ...

1 School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China 2 State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China ...

Abstract: cross-dimensional principal component analysis (CD-PCA). It is based on the semi-tensor product of matrices theory (STP), where a new projection rule is introduced to reduce dimensionality ...