# Missing Attribute Clustering Eval

> Tests clustering and classification algorithms on tabular datasets containing missing attributes (non-existence attributes). It evaluates how well direct discrepancy-based methods handle missing data under four simulated mechanisms (MCAR, MAR, MNAR-1, MNAR-2) compared to traditional imputation baselines. Use when the user wants to benchmark on Iris, Sonar, Glass, Leaf, Seeds, Libras, Chronic Kidney, Vowel Context, Isolate, Landsat, Breast Tissue, Bank note, or asks about evaluating this task. Reports accuracy_rate.

- Skill: `qhjqhj00/missing-attribute-clustering-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/missing-attribute-clustering-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/missing-attribute-clustering-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/missing-attribute-clustering-eval

---


# missing-attribute-clustering-eval

> Clustering and Classification with Non-Existence Attributes: A Sentenced Discrepancy Measure Based Technique — Joarder et al. (2020) (arXiv:2002.10411, 2020)

## What this evaluates

Tests clustering and classification algorithms on tabular datasets containing missing attributes (non-existence attributes). It evaluates how well direct discrepancy-based methods handle missing data under four simulated mechanisms (MCAR, MAR, MNAR-1, MNAR-2) compared to traditional imputation baselines.

## Datasets

- **Iris** — total 150; splits: full (150)
- **Sonar** — total 208; splits: full (208)
- **Glass** — total 214; splits: full (214)
- **Leaf** — total 340; splits: full (340)
- **Seeds** — total 210; splits: full (210)
- **Libras** — total 360; splits: full (360)
- **Chronic Kidney** — total 800; splits: full (800)
- **Vowel Context** — total 990; splits: full (990)
- **Isolate** — total 1559; splits: full (1559)
- **Landsat** — total 6435; splits: full (6435)
- **Breast Tissue** — total 106; splits: full (106)
- **Bank note** — total 1372; splits: full (1372)

## Metrics

- `accuracy_rate` **(primary)** — range: [0, 1]
  - Calculated as the proportion of correctly assigned cluster labels or class labels out of the total number of instances. No symbolic formula is provided in the paper; it follows standard classification/clustering accuracy conventions.

## Input / output format

**Input**: Tabular feature vectors with some attributes explicitly removed (non-existence) according to MCAR, MAR, MNAR-1, or MNAR-2 mechanisms. All features are normalized to zero mean and unit standard deviation prior to evaluation.

**Output**: Predicted cluster labels or class labels for each instance.

## Scoring recipe

```python
def accuracy_rate(predictions, gold):
    correct = sum(1 for p, g in zip(predictions, gold) if p == g)
    return correct / len(gold)
```

## Common pitfalls

- Missing data is artificially simulated under specific mechanisms rather than naturally occurring, which may limit real-world generalizability.
- All features are normalized to zero mean and unit standard deviation before evaluation, potentially obscuring scale-dependent algorithm behaviors.
- Results are reported as mean ± standard deviation, but the number of random seeds, initialization runs, or cross-validation folds is not explicitly stated.

## Evidence (verbatim from paper)

> The table and graphs represent the accuracy rate performances accuracy of Non-Existence attributes by MCAR Non-Existence using Scalable K-MEANS++-AWPD, K-MEANS++-AWPD, K-MEANS-FWPD and Imputation method with K-MEANS clustering algorithm.

## Citation

```bibtex
@misc{joarder2020clustering,
  title={Clustering and Classification with Non-Existence Attributes: A Sentenced Discrepancy Measure Based Technique},
  author={Joarder et al. (2020)},
  year={2020},
  note={arXiv:2002.10411}
}
```

- arXiv: 2002.10411