AI Lesson 33 – K-Means Clustering | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

K-Means Clustering

K-Means Clustering is an unsupervised learning algorithm used to group similar data points into clusters. Unlike supervised learning, K-Means works without labeled output data.

The goal of K-Means is simple: group data points so that points in the same cluster are more similar to each other than to points in other clusters.

Why Do We Need Clustering?

In many real-world problems, we do not know the correct output in advance. We only have raw data and want to discover hidden patterns or structures.

Examples include:

  • Customer segmentation in marketing
  • Grouping similar news articles
  • Image compression
  • Finding usage patterns in applications

Real-World Example

Suppose an e-commerce company wants to group customers based on age and income. The company does not know the groups beforehand but wants to identify patterns like low-income, medium-income, and high-income customers.

K-Means automatically finds these groups based on similarity.

What Does “K” Mean?

The letter K represents the number of clusters we want to form. This value is chosen by the user.

For example:

  • K = 2 → 2 clusters
  • K = 3 → 3 clusters
  • K = 5 → 5 clusters

How K-Means Works

K-Means follows an iterative process:

  • Choose K initial centroids randomly
  • Assign each data point to the nearest centroid
  • Recalculate centroids based on cluster averages
  • Repeat until centroids stop changing

This process minimizes the distance between data points and their assigned cluster centers.

K-Means Clustering Example

Below is a simple example using customer data with age and income. 


from sklearn.cluster import KMeans

# Sample data: [Age, Income]
X = [
    [25, 30000],
    [30, 40000],
    [35, 60000],
    [40, 65000],
    [45, 80000],
    [50, 90000]
]

# Create K-Means model
model = KMeans(n_clusters=2, random_state=42)

# Train model
model.fit(X)

# Predict cluster labels
labels = model.predict(X)
print(labels)
[0 0 1 1 1 1]

The output shows which cluster each customer belongs to. Customers with similar age and income are grouped together automatically.

Understanding the Output

Each number represents a cluster ID. Data points with the same label belong to the same cluster.

Cluster numbers themselves have no meaning — only the grouping matters.

Choosing the Right Value of K

Choosing the correct number of clusters is important. A common method is the Elbow Method.

In this method, we run K-Means for multiple K values and choose the point where improvement slows down.

Advantages of K-Means

  • Simple and easy to understand
  • Fast and scalable
  • Works well for large datasets

Limitations of K-Means

  • Must choose K in advance
  • Sensitive to outliers
  • Works best with spherical clusters

Practice Questions

Practice 1: K-Means belongs to which type of learning?



Practice 2: What does K-Means try to form?



Practice 3: What represents the center of each cluster?



Quick Quiz

Quiz 1: What does the value K represent?





Quiz 2: K-Means works using which process?





Quiz 3: Which method helps choose the value of K?





Coming up next: Hierarchical Clustering — building clusters step by step without choosing K in advance.

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