KMeans Clustering with Iris Dataset
K-means clustering is an Unsupervised machine learning algorithm.
- First, choose the clusters K
- Randomly select k centroids from the whole dataset
- Assign all points to the closest cluster centroid
- Recompute centroids again for new clusters
- now repeat steps 3 and 4 until centroids converge
Python3
wcss = [] for i in range(1, 11): kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0) kmeans.fit(x) wcss.append(kmeans.inertia_) # from above array with help of elbow method #we can get no of cluster to provide. kmeans = KMeans(n_clusters=3, init='k-means++', max_iter=300, n_init=10, random_state=0) y_kmeans = kmeans.fit_predict(x) |
In the above code, we have used the elbow method to get the optimized value of k. If we plot a graph for it we get a value of 3.
Visualizing the Clusters
Python3
# Visualising the clusters cols = iris.columns plt.scatter(X.loc[y_kmeans == 0, cols[0]], X.loc[y_kmeans == 0, cols[1]], s=100, c='purple', label='Iris-setosa') plt.scatter(X.loc[y_kmeans == 1, cols[0]], X.loc[y_kmeans == 1, cols[1]], s=100, c='orange', label='Iris-versicolour') plt.scatter(X.loc[y_kmeans == 2, cols[0]], X.loc[y_kmeans == 2, cols[1]], s=100, c='green', label='Iris-virginica') # Plotting the centroids of the clusters plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c='red', label='Centroids') plt.legend() |
Output:
Clusters obtained by using the K-means algorithm
Accuracy and Performance of Model
Now let’s check the performance of the model.
Python3
pd.crosstab(iris.target, y_kmeans) |
Output:
As the algorithm is an unsupervised algorithm we don’t have test data here to check the performance of the model on it. Setosa class is clustered perfectly. While Versicolor has only 2 misclassifications. Class virginica is getting overlapped Versicolor hence there is 14 misclassifications.
Analyzing Decision Tree and K-means Clustering using Iris dataset
Iris Dataset is one of best know datasets in pattern recognition literature. This dataset contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2 the latter are NOT linearly separable from each other.
Attribute Information:
- Sepal Length in cm
- Sepal Width in cm
- Petal Length in cm
- al Width in cm
- Class:
- Iris Setosa
- Iris Versicolour
- Iris Virginica
Let’s perform Exploratory data analysis on the dataset to get our initial investigation right.