🔗 MemoLearning Clustering Techniques

Discover hidden patterns and group similar data points using unsupervised learning

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Clustering Techniques Curriculum

11
Core Units
~65
Clustering Concepts
10+
Algorithms
15+
Evaluation Metrics
1

Introduction to Clustering

Understand the fundamentals of clustering as an unsupervised learning technique for pattern discovery.

  • What is clustering
  • Unsupervised vs supervised learning
  • Types of clustering problems
  • Similarity and distance measures
  • Cluster characteristics
  • Applications of clustering
  • Challenges in clustering
  • Exploratory data analysis
2

K-Means Clustering

Master the most popular clustering algorithm for partitioning data into k clusters.

  • K-means algorithm steps
  • Centroid initialization
  • Lloyd's algorithm
  • Choosing optimal k
  • Elbow method
  • Silhouette analysis
  • K-means++ initialization
  • Limitations and assumptions
3

Hierarchical Clustering

Learn hierarchical clustering methods for creating tree-like cluster structures.

  • Agglomerative clustering
  • Divisive clustering
  • Linkage criteria
  • Dendrograms
  • Distance metrics
  • Cutting the dendrogram
  • Ward linkage
  • Computational complexity
4

DBSCAN and Density-Based Clustering

Explore density-based clustering for finding arbitrarily shaped clusters and handling noise.

  • Density-based clustering concepts
  • DBSCAN algorithm
  • Core points and border points
  • Epsilon and MinPts parameters
  • Handling noise and outliers
  • OPTICS algorithm
  • HDBSCAN improvements
  • Parameter selection strategies
5

Gaussian Mixture Models

Understand probabilistic clustering using Gaussian Mixture Models and the EM algorithm.

  • Probabilistic clustering
  • Gaussian distributions
  • Mixture models
  • Expectation-Maximization algorithm
  • Soft clustering assignments
  • Model selection criteria
  • Covariance types
  • Bayesian information criterion
6

Mean Shift and Mode-Seeking

Learn mean shift clustering for finding dense regions and cluster centers automatically.

  • Mean shift algorithm
  • Kernel density estimation
  • Bandwidth selection
  • Mode-seeking behavior
  • Automatic cluster detection
  • Applications in computer vision
  • Computational considerations
  • Comparison with other methods
7

Spectral Clustering

Explore advanced clustering using graph theory and eigenvalue decomposition.

  • Graph-based clustering
  • Similarity graphs
  • Laplacian matrices
  • Eigenvalue decomposition
  • Normalized cuts
  • Affinity matrices
  • Non-convex cluster shapes
  • Parameter tuning
8

Cluster Evaluation

Learn methods to evaluate clustering quality and compare different clustering solutions.

  • Internal validation measures
  • External validation measures
  • Silhouette coefficient
  • Calinski-Harabasz index
  • Davies-Bouldin index
  • Adjusted Rand index
  • Normalized mutual information
  • Visual evaluation techniques
9

High-Dimensional Clustering

Address challenges and techniques for clustering in high-dimensional spaces.

  • Curse of dimensionality
  • Distance concentration
  • Dimensionality reduction preprocessing
  • Subspace clustering
  • Projected clustering
  • Feature selection for clustering
  • PCA and clustering
  • Manifold-based clustering
10

Time Series and Stream Clustering

Learn specialized clustering techniques for temporal data and streaming data.

  • Time series clustering
  • Dynamic time warping
  • Shape-based clustering
  • Stream clustering algorithms
  • Online clustering
  • Concept drift handling
  • Window-based approaches
  • Real-time applications
11

Practical Applications

Apply clustering techniques to real-world problems and learn best practices for implementation.

  • Customer segmentation
  • Market research applications
  • Image segmentation
  • Gene expression analysis
  • Social network analysis
  • Anomaly detection
  • Data preprocessing strategies
  • Scalability considerations

Unit 1: Introduction to Clustering

Understand the fundamentals of clustering as an unsupervised learning technique for pattern discovery.

What is Clustering

Learn clustering as the task of grouping similar data points together without predefined labels.

Unsupervised Pattern Discovery Grouping
Clustering algorithms automatically discover hidden structures in data by grouping similar observations together based on their features.

Unsupervised vs Supervised Learning

Understand the key differences between supervised learning (with labels) and unsupervised learning (without labels).

# Supervised learning (with labels)
X = [[1, 2], [2, 3], [3, 4]]
y = [0, 1, 0] # Known labels

# Unsupervised learning (no labels)
X = [[1, 2], [2, 3], [3, 4]]
# No y - algorithm finds patterns

Types of Clustering Problems

Explore different types of clustering based on cluster structure and overlap.

Hard Clustering: Each point belongs to exactly one cluster
Soft Clustering: Points can belong to multiple clusters with probabilities
Hierarchical: Nested cluster structures
from sklearn.cluster import KMeans
# Hard clustering example
kmeans = KMeans(n_clusters=3)
labels = kmeans.fit_predict(X) # [0, 1, 2, 0, 1]

Similarity and Distance Measures

Learn various metrics to measure similarity and distance between data points.

Euclidean Manhattan Cosine
from scipy.spatial.distance import pdist
import numpy as np

# Calculate distances
data = np.array([[1, 2], [3, 4], [5, 6]])
distances = pdist(data, metric='euclidean')

Cluster Characteristics

Understand what makes a good cluster: compactness, separation, and connectivity.

Compactness: Points within a cluster are close together
Separation: Different clusters are far apart
Connectivity: Points in a cluster are connected
# Measure cluster quality
from sklearn.metrics import silhouette_score
score = silhouette_score(X, labels)
print(f"Silhouette Score: {score:.3f}")

Applications of Clustering

Explore real-world applications where clustering provides valuable insights.

• Customer segmentation for marketing
• Gene expression analysis
• Image segmentation
• Document organization
• Social network analysis
• Anomaly detection

Challenges in Clustering

Learn about common challenges and limitations when applying clustering algorithms.

• Determining the number of clusters
• Handling different cluster shapes and sizes
• Dealing with noise and outliers
• Curse of dimensionality
• Scalability to large datasets

Exploratory Data Analysis

Use clustering as an exploratory tool to understand data structure and generate hypotheses.

import matplotlib.pyplot as plt
import seaborn as sns

# Visualize clusters
plt.figure(figsize=(10, 6))
sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=labels)
plt.title('Cluster Visualization')

Unit 2: K-Means Clustering

Master the most popular clustering algorithm for partitioning data into k clusters.

K-Means Algorithm Steps

Learn the iterative process of K-means clustering and how it converges to a solution.

1. Initialize k cluster centroids randomly
2. Assign each point to nearest centroid
3. Update centroids to mean of assigned points
4. Repeat steps 2-3 until convergence
from sklearn.cluster import KMeans
import numpy as np

# Basic K-means implementation
kmeans = KMeans(n_clusters=3, random_state=42)
labels = kmeans.fit_predict(X)
centroids = kmeans.cluster_centers_

Centroid Initialization

Understand different methods for initializing cluster centroids and their impact on results.

# Different initialization methods
# Random initialization
kmeans_random = KMeans(n_clusters=3, init='random')

# K-means++ initialization (default)
kmeans_plus = KMeans(n_clusters=3, init='k-means++')

Lloyd's Algorithm

Learn the mathematical foundation of the standard K-means algorithm.

Lloyd's algorithm minimizes the within-cluster sum of squares (WCSS) by iteratively updating cluster assignments and centroids.
# Monitor convergence
kmeans = KMeans(n_clusters=3, max_iter=300, tol=1e-4)
kmeans.fit(X)
print(f"Converged in {kmeans.n_iter_} iterations")

Choosing Optimal K

Learn methods to determine the optimal number of clusters for your dataset.

Elbow Method