Standardization transforms numeric variables onto a comparable scale by centering and scaling their variability. It prevents naturally larger dimensions from dominating distance calculations. section 2.3.5
A practical clustering workflow is:
- Inspect the dataset, class distribution, plots, and missing-value counts before transformation. section 2.3.5
- Remove the nonnumeric class column, then standardize the remaining features with scipy.stats.zscore or sklearn.preprocessing.StandardScaler. section 2.3.5
- Plot the standardized features and use Euclidean-distance distortions with the elbow method before fitting k-means. section 2.3.5
Vehicle clustering
For vehicle data, standardize every feature after excluding class, inspect X_standard with a scatter plot, and use it as k-means input. The elbow plot indicates three clusters as a suitable choice. section 2.3.5
import pandas as pd
vehicle_df = pd.read_csv('vehicle.csv').dropna()
Start with the supplied vehicle-data loading and cleaning workflow before checking and standardizing features. section 2.3.5
