Implement simple K-Means Algorithm to demonstrate the clustering rule

Question

Implement simple K-Means Algorithm to demonstrate the clustering rule on the following datasets:
(a) iris.arff
(b) student.arff

25 Jul 2024

Answer :

Word Count : 845

Implementing the K-Means algorithm involves clustering data into distinct groups based on their features. Let's walk through this process using the Iris and Student datasets in the ARFF (Attribute-Relation File Format) format.

### Step-by-Step Implementation of K-Means Algorithm

#### Step 1: Understanding the K-Means Algorithm
The K-Means algorithm is a popular clustering method that partitions a dataset into K clusters. Each cluster has a centroid, and the algorithm assigns data points to clusters such that the sum of squared distances from each point to the centroid is minimized. The steps involved in K-Means are:
1. Initialize K centroids randomly.
2. Assign each data point to the nearest centroid, forming K clusters.
3. Recalculate the centroids of the newly formed clusters.
4. Repeat steps 2 and 3 until the centroids no longer change significantly or a maximum number of iterations is reached.

#### Step 2: Loading ARFF Datasets
We will use the `scipy` library to read the ARFF files and convert them into a format suitable for our implementation.

#### Step 3: Implementing the K-Means Algorithm
We will implement the K-Means algorithm from scratch and apply it to both datasets.

Here’s a detailed implementation:

```python
import numpy as np
from scipy.io import arff
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