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

- **ID:** `opencv/kmeans-invalid-clusters`
- **Domain:** opencv
- **Category:** assertion_error
- **Error Code:** `-215`
- **Verification:** ai_generated
- **Fix Rate:** 90%

## Root Cause

The number of data points (N) is less than the number of requested clusters (K), making clustering impossible.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| 4.5.0 | active | — | — |
| 4.6.0 | active | — | — |
| 4.7.0 | active | — | — |
| 4.8.0 | active | — | — |
| 4.9.0 | active | — | — |

## Workarounds

1. **Reduce the number of clusters K to be at most the number of data points N. Check N before calling kmeans and clamp K accordingly.** (95% success)
   ```
   Reduce the number of clusters K to be at most the number of data points N. Check N before calling kmeans and clamp K accordingly.
   ```
2. **Add more data points by collecting additional samples or using data augmentation to increase N above K.** (70% success)
   ```
   Add more data points by collecting additional samples or using data augmentation to increase N above K.
   ```

## Dead Ends

- **Increase the number of data points by duplicating existing points** — Duplicating points does not add new information and can bias the clustering; the real fix is to reduce K or use more distinct data. (70% fail)
- **Use a different clustering algorithm like DBSCAN** — While this avoids the error, it changes the algorithm behavior and may not be appropriate for the use case. (40% fail)
- **Set K to a very large number** — Making K larger only exacerbates the N >= K violation. (95% fail)