Feature Extraction Using Principal Components in PCA Analysis

Prior to processing raw data, normalization is essential to ensure comparison and analysis occur on a consistent scale. This preprocessing step typically involves z-score standardization or min-max scaling to eliminate dimensional influences. Subsequently, Principal Component Analysis (PCA) is applied for dimensionality reduction, where eigenvalues and eigenvectors are computed from the covariance matrix to identify the most significant principal components (PCs). These PCs, representing directions of maximum variance, serve as optimized features for subsequent analysis. Finally, fuzzy clustering analysis employing the fuzzy c-means algorithm groups data points with membership degrees, allowing soft clustering where elements can belong to multiple clusters simultaneously. This approach utilizes iterative optimization of cluster centroids and membership matrices to reveal data relationships and trends, supporting informed decision-making through probabilistic cluster assignments.