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Exploring Data Mining Techniques: From Iris Classification to Swarm Intelligence

This document discusses various data mining strategies, particularly focusing on the Iris flower dataset and the creation of decision trees and artificial neural networks. It highlights the self-organizing principles seen in natural systems like bee and termite colonies, illustrating how complex structures emerge from simple interactions. The concept of minimum spanning trees (MST) is introduced, explaining their importance in graph theory and data analysis. Additionally, it touches on double bridge experiments with real ants and their implications in expert systems.

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Exploring Data Mining Techniques: From Iris Classification to Swarm Intelligence

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  1. Data Mining

  2. Data Mining

  3. Data Mining

  4. Data Mining

  5. Data Mining

  6. Data Mining

  7. Data Mining

  8. Data Mining

  9. One Possible Solution

  10. Iris Flower

  11. Iris Flower

  12. Data Mining

  13. Data Mining

  14. Data Mining

  15. Creating Trees

  16. Creating Trees

  17. Creating Trees

  18. Creating Trees

  19. Creating Trees

  20. Artificial Neural Networks

  21. Swarm Intelligence • In a Bee Colony, who knows the blue print of the nest? The queen or hive? • In a Termite Nest knows the blue print of the nest?

  22. Self-organizing processes exhibit emergent properties. • The resulting structures appear to be more complex than what could be provided by individual contributions. • Given a connected, undirected graph, a spanning tree of that graph is a subgraph that is a tree and connects all the vertices together • A minimum spanning tree (MST) or minimum weight spanning tree is then a spanning tree with weight less than or equal to the weight of every other spanning tree. • http://en.wikipedia.org/wiki/Minimum_spanning_tree

  23. Double Bridge Experiments (Real Ants)

  24. Expert Systems

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