To validate product clustering accuracy, test cases should focus on verifying that the algorithm correctly groups similar products and distinguishes dissimilar ones. Key tests include checking exact matches, handling edge cases, and measuring performance against labeled datasets. Each test should confirm that the clustering logic aligns with the product attributes and business goals, such as improving search relevance or inventory management.
First, test basic clustering logic by verifying that identical or nearly identical products are grouped together. For example, two shirts with the same brand, size, color, and material should belong to the same cluster. Conversely, products with significant differences—like a shirt and a pair of shoes—should never be clustered together. Include cases where products share partial attributes (e.g., same brand but different categories) to ensure the algorithm prioritizes the right features. For instance, a “Stanley water bottle” and a “Stanley hammer” should be separated despite sharing the brand name. These tests validate whether the clustering logic correctly weighs attributes like category, brand, and specifications.
Next, test edge cases and data quality issues. For example, handle products with missing or ambiguous attributes (e.g., a “black dress” with no size or material details) to ensure the algorithm either defaults to a reasonable cluster or flags incomplete data. Check how the system handles typos or variations in product names (e.g., “iPhone 12” vs. “IPhone12”) by normalizing inputs before clustering. Test scalability by clustering large datasets (e.g., 10,000 products) to verify performance and consistency. Additionally, validate dynamic updates: if a product’s attributes change (e.g., a price drop), ensure it doesn’t shift clusters unless the change is significant enough to alter its categorization.
Finally, use labeled datasets to measure precision, recall, and F1-score. For example, if 95 out of 100 known “kitchen knives” are correctly grouped, precision is 95%. Compare the algorithm’s clusters against a manually verified ground truth using metrics like the Adjusted Rand Index (ARI) to quantify alignment. Test cross-category leakage—like a “blender” appearing in both “appliances” and “kitware” clusters—and ensure the logic avoids overlaps unless intentional. For multilingual or multi-region catalogs, validate that translations (e.g., “phone” vs. “teléfono”) don’t disrupt clustering. These metrics and scenarios ensure the system meets both technical and business requirements reliably.