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50 Algorithms Every Programmer Should Know

You're reading from   50 Algorithms Every Programmer Should Know Tackle computer science challenges with classic to modern algorithms in machine learning, software design, data systems, and cryptography

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803247762
Length 538 pages
Edition 2nd Edition
Languages
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Author (1):
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Imran Ahmad Imran Ahmad
Author Profile Icon Imran Ahmad
Imran Ahmad
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Toc

Table of Contents (22) Chapters Close

Preface 1. Section 1: Fundamentals and Core Algorithms FREE CHAPTER
2. Overview of Algorithms 3. Data Structures Used in Algorithms 4. Sorting and Searching Algorithms 5. Designing Algorithms 6. Graph Algorithms 7. Section 2: Machine Learning Algorithms
8. Unsupervised Machine Learning Algorithms 9. Traditional Supervised Learning Algorithms 10. Neural Network Algorithms 11. Algorithms for Natural Language Processing 12. Understanding Sequential Models 13. Advanced Sequential Modeling Algorithms 14. Section 3: Advanced Topics
15. Recommendation Engines 16. Algorithmic Strategies for Data Handling 17. Cryptography 18. Large-Scale Algorithms 19. Practical Considerations 20. Other Books You May Enjoy
21. Index

Evaluating the clusters

The objective of good quality clustering is that the data points that belong to the separate clusters should be differentiable. This implies the following:

  • The data points that belong to the same cluster should be as similar as possible.
  • Data points that belong to separate clusters should be as different as possible.

Human intuition can be used to evaluate the clustering results by visualizing the clusters, but there are mathematical methods that can quantify the quality of the clusters. They not only measure the tightness of each cluster (cohesion) and the separation between different clusters but also offer a numerical, hence objective, way to assess the quality of clustering. Silhouette analysis is one such technique that compares the tightness and separation in the clusters created by the k-means algorithm. It’s a metric that quantifies the degree of cohesion and separation in clusters. While this technique has been mentioned...

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