K-means Clustering › Data Requirements and Pre-processing

K-means requires numerical data with features that are comparable in scale, making data standardization critical for successful clustering. Since the algorithm uses Euclidean distance calculations, features with larger scales will dominate the clustering process, potentially leading to misleading results. Apply StandardScaler or MinMaxScaler before clustering to ensure all features contribute equally to the distance calculations. The algorithm assumes that all features are equally important, so consider using feature selection techniques or dimensionality reduction methods like PCA if you have many irrelevant or highly correlated features.

The algorithm works best with clean, complete datasets that have minimal missing values and outliers. Missing values should be imputed using appropriate strategies (mean, median, or more sophisticated methods), and outliers should be identified and either removed or handled carefully, as they can significantly affect centroid positions and cluster boundaries. Categorical variables need to be encoded (using one-hot encoding or label encoding) before clustering, and the choice of encoding method can impact results. Ensure your dataset has sufficient data points relative to the number of features to avoid the curse of dimensionality, which can lead to poor clustering performance in high-dimensional spaces.