Questions

To implement a simple K-Means algorithm for clustering the datasets iris.arff and student.arff, you'll need a few key tools. Here's a step-by-step guide:

Step 1: Setup Your Environment

Ensure you have Python installed along with the necessary libraries, such as numpy, pandas, and scikit-learn.

pip install numpy pandas scikit-learn

Step 2: Load the Datasets

You will need the liac-arff library to read .arff files.

pip install liac-arff

Step 3: Implement the K-Means Algorithm

Here's a Python script that demonstrates a simple K-Means clustering on the datasets:

import numpy as np
import pandas as pd
import arff
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Function to load ARFF file and convert to DataFrame
def load_arff_to_dataframe(file_path):
    with open(file_path, 'r') as file:
        data = arff.load(file)
    columns = [attr[0] for attr in data['attributes']]
    df = pd.DataFrame(data['data'], columns=columns)
    return df

# Function to perform K-Means clustering
def perform_kmeans_clustering(data, n_clusters, features):
    # Standardize features
    scaler = StandardScaler()
    scaled_features = scaler.fit_transform(data[features])

    # Apply K-Means
    kmeans = KMeans(n_clusters=n_clusters, random_state=42)
    data['Cluster'] = kmeans.fit_predict(scaled_features)

    return data, kmeans

# Load datasets
iris_df = load_arff_to_dataframe("iris.arff")
student_df = load_arff_to_dataframe("student.arff")

# Define features for clustering
iris_features = iris_df.columns[:-1]  # Assuming last column is the target or non-feature
student_features = student_df.columns  # Adjust accordingly based on the dataset

# Perform K-Means clustering
iris_clusters, iris_kmeans = perform_kmeans_clustering(iris_df, n_clusters=3, features=iris_features)
student_clusters, student_kmeans = perform_kmeans_clustering(student_df, n_clusters=3, features=student_features)

# Print results
print("Iris Dataset Clusters:")
print(iris_clusters[['Cluster']].head())

print("\nStudent Dataset Clusters:")
print(student_clusters[['Cluster']].head())

Step 4: Adjust Parameters

• You may need to adjust the number of clusters (n_clusters) based on your dataset understanding.
• Check the dataset features to ensure correct feature selection.

Step 5: Evaluate and Visualize

Evaluate the clustering results by comparing against the known class labels (for the iris dataset) or by visualizing the cluster centers and data distribution.

Step 6: Run the Script

Execute the Python script to perform K-Means clustering on your ARFF datasets. Make sure your datasets (iris.arff and student.arff) are in the correct directory or provide the full path.

This will give you a basic implementation of K-Means clustering on the given datasets. Adjust according to specific feature requirements or dataset structure.