# cv2.error: OpenCV(4.9.0) /tmp/opencv-4.9.0/modules/core/src/kmeans.cpp:245: error: (-215:Assertion failed) N >= K in function 'kmeans'

- **ID:** `opencv/kmeans-clustering-empty-labels`
- **Domain:** opencv
- **Category:** assertion_error
- **Error Code:** `-215`
- **Verification:** ai_generated
- **Fix Rate:** 95%

## Root Cause

Number of data points (N) is less than the number of clusters (K) requested in k-means clustering, causing an assertion failure.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| 4.8.0 | active | — | — |
| 4.9.0 | active | — | — |
| 4.10.0 | active | — | — |

## Workarounds

1. **Ensure K is less than or equal to the number of data points. Add a check: `if len(data) < K: K = len(data)` before calling kmeans.** (95% success)
   ```
   Ensure K is less than or equal to the number of data points. Add a check: `if len(data) < K: K = len(data)` before calling kmeans.
   ```
2. **Use a smaller K value appropriate for the dataset: `K = min(K, len(data))`** (90% success)
   ```
   Use a smaller K value appropriate for the dataset: `K = min(K, len(data))`
   ```
3. **Collect more data points or use a different clustering algorithm (e.g., DBSCAN) that doesn't require specifying K.** (70% success)
   ```
   Collect more data points or use a different clustering algorithm (e.g., DBSCAN) that doesn't require specifying K.
   ```

## Dead Ends

- **** — Using K=1 might avoid the assertion but is not meaningful for clustering; it's a workaround but not a fix. (40% fail)
- **** — Randomly duplicating points to increase N distorts the data distribution and produces incorrect clusters. (80% fail)
- **** — Transposing the data matrix doesn't change the number of samples; it only changes feature dimensions. (70% fail)