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+title = "🌸 Clustering Iris Species with K-Means and Hierarchical Methods"
+description = "A hands-on comparison of K-Means and Agglomerative Clustering on the Iris dataset, with insights into performance, parameter tuning, and practical trade-offs."
+date = 2025-11-01
+[taxonomies]
+tags = ["machine_learning"]
+[extra]
+styles = ["notebooks.css", ]
++++
+
+## Why Clustering?
+
+Clustering is a foundational technique in unsupervised learning, used to
+uncover patterns in data without predefined labels. This project explores how
+two popular clustering algorithms—**K-Means** and **Agglomerative Hierarchical
+Clustering**—perform on the well-known **Iris flower dataset**, aiming to group
+samples by species based solely on their morphological features.
+
+***
+
+## About the Dataset
+
+The Iris dataset contains **150 samples** from three species: *setosa*,
+*versicolor*, and *virginica*. Each sample includes four features:
+
+* Sepal length
+* Sepal width
+* Petal length
+* Petal width
+
+While *setosa* is linearly separable, *versicolor* and *virginica* overlap
+significantly, making this dataset ideal for testing clustering algorithms.
+
+***
+
+## What Was Explored
+
+The analysis focused on:
+
+* Comparing **K-Means** and **Agglomerative Clustering**
+* Evaluating performance using **accuracy**, **silhouette score**, and
+**Adjusted Rand Index (ARI)**
+* Testing different **linkage methods** and **distance metrics**
+* Visualizing clusters and errors using **PCA**
+
+***
+
+## Key Experiments & Findings
+
+### Dimensionality Reduction with PCA
+
+* **95.8%** of the variance was captured using just **2 principal components**, confirming
+strong correlations among features—especially between petal length and width.
+
+### Optimal Cluster Count
+
+* Using metrics like **inertia**, **silhouette score**, and **accuracy**, the optimal
+number of clusters was found to be **3**, matching the true number of species.
+
+### Parameter Tuning for Agglomerative Clustering
+
+* Tried combinations of:
+  * Linkage: `ward`, `complete`, `average`, `single`
+  * Metrics: `euclidean`, `manhattan`, `cosine`
+* **Best result**: `average linkage` with `manhattan distance` achieved **88.7%
+accuracy**, outperforming default settings.
+
+### Performance Comparison
+
+| Algorithm               | Accuracy | Silhouette Score | ARI  |
+| ----------------------- | :------: | :--------------: | :--: |
+| K-Means                 | 83.3%    | 0.46             | 0.62 |
+| Agglomerative (default) | 82.7%    | 0.45             | 0.61 |
+| Agglomerative (best)    | 88.7%    | 0.45             | 0.72 |
+
+***
+
+## Error Analysis
+
+* **Setosa** was classified almost perfectly across all methods.
+* Most errors occurred between **versicolor** and **virginica**, confirming
+their overlapping nature.
+* Agglomerative Clustering showed **bias** depending on parameters—sometimes
+misclassifying one species more than the other.
+
+***
+
+## Final Thoughts
+
+While Agglomerative Clustering achieved the highest accuracy with tuned
+parameters, its **sensitivity to configuration** and **instability** in cluster
+composition make it less reliable for real-world applications without labeled
+data.
+
+**K-Means**, despite slightly lower accuracy, offered **more balanced results**
+and **greater stability**, making it a safer choice for practical clustering
+tasks.
+
+***
+
+## Future Work
+
+* Extend analysis to other clustering algorithms like DBSCAN or Spectral Clustering
+* Apply to more complex datasets
+* Explore automated parameter tuning techniques
+
+***
+
+The full notebook with code and visualizations is embedded below 👇
+
+<!-- markdownlint-disable MD033 -->
+<iframe title="Spam Email Classification notebook" class="notebook-embed" src="notebook.html"></iframe>
+
+You can also view the notebook in [a separate page](notebook.html), or check it
+on [GitHub](https://github.com/Farzat07/Unsupervised-Learning-Final-Project-Iris-Species-Clustering-Analysis).