This example demonstrates centroid updates through a concrete numerical problem.
Step 1: Problem Setup
Define the input features, output targets, and model structure.
Step 2: Apply Algorithm
Execute the Clustering Basics algorithm following the mathematical derivation from the course.
Step 3: Evaluate Results
Analyze the model performance using appropriate metrics and visualizations.
Apply Clustering Basics to a complex dataset with multiple features and evaluate different parameter settings.
Dataset size: 1000 samples, 20 features
Training time: Analyze computational complexity
Performance: Compare with baseline methods
Insights: Discover patterns and optimize hyperparameters
Explain the key concepts of Clustering Basics and compare with alternative approaches.
Review the mathematical derivations and core principles from the course material.
Implement Clustering Basics from scratch and test on a sample dataset.
import numpy as np
class Model:
def __init__(self, params):
self.params = params
def fit(self, X_train, y_train):
# Training logic
pass
def predict(self, X_test):
# Prediction logic
pass
def evaluate(self, X, y):
# Evaluation metrics
pass
Apply Clustering Basics to a real-world dataset and analyze the results.