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Visual Clustering with Artificial Ants Colonies

Visual Clustering with Artificial Ants Colonies.

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Visual Clustering with Artificial Ants Colonies

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  1. Visual Clustering with Artificial Ants Colonies N. Labroche, N. Monmarché and G. VenturiniLaboratoire d'Informatique de l'Université de ToursÉcole Polytechnique de l'Université de Tours – Département Informatique64, avenue Jean Portalis 37200 Tours, France{labroche,monmarche,venturini}@univ-tours.fr

  2. Talk overview • Goal • ant-based clustering algorithms • the chemical recognition system of ants • Main principles of our model • Visual AntClust algorithms • Results and example • Conclusion

  3. Goal • Building a visual clustering tool • Idea: • Real ants solve a clustering problem in their everyday life  nestmates recognition mechanism • Method: • Modelling the chemical recognition system of real ants • Extracting its main principles to create a new unsupervised clustering algorithm

  4. Clustering Problem

  5. Ant-based clustering algorithms (1/3) • Brood sorting: Lumer and Faieta (1994) • Discrete grid on which ants move, pick up or drop randomly placed objects • Problem: two contiguous sets of objects can be considered as only one set • AntClass: Monmarché (2000) • Hybridisation with k-Means • Several objects on the same place

  6. Ant-based clustering algorithms (2/3)

  7. ant-based clustering algorithms (3/3) • Topic maps for Web pages: J. Handl (2002) • Behavioural switches ("Eager ants", "Jumps") • Acluster: V. Ramos (2002) • + objects in the neighborhood  + Pheromones trails

  8. Main principles of the chemical recognition system of ants Cuticular odour or « label » (hydrocarbons) Neuronal template Recognition: phenotype matching mechanism  comparison between label and template A set of behavioural rules (aggression, reject, feeding, social licking, trophallaxy, …) Genome

  9. Model of the chemical recognition system of ants Satisfaction estimator s Label = 2D-vector Template = Acceptance threshold Acceptance mechanism Behavioural rules = "Meeting" algorithm Genome = one object of the data set

  10. Template learning: principles • Each ant a performs NL meetings • Mean similarity • Maximal similarity • Template for ant a is defined as:

  11. Acceptance mechanism • Acceptance between 2 ants a and b

  12. Visual AntClust Main Algorithm Initialize N ants While NbIter iterations are not reached Draw N ants in the 2D-odour space Repeat N times Meeting(a,b), a,b randomly chosen ants End While Group in the same nest all the ants within a perimeter of value Dmax Delete the nests that are too small Reassign the ants with no more nest

  13. Meeting (Ant a, Ant b) D  Euclidian distance between Labela and Labelb D <= (1-max(sa,sb)) And Acceptance(a,b) Yes No Increase sa and sb ants a and b are well-placed Update Labela(b) according to Ra(b) End

  14. Experiments • Visual AntClust is compared to: • K-Means • AntClass • AntClust: an other ant-based clustering algorithm inspired by a discret modelling of the chemical recognition system • 50 runs for each method and each data set

  15. Data sets

  16. Clustering Error Measure c : expected cluster label c’ : computed cluster label

  17. Results (1/2)

  18. Results (2/2)

  19. Example 1 Step 1:

  20. Example 1 Step 2:

  21. Example 1 Step 3:

  22. Example 1 Step 4:

  23. Example 1 Art 6 data set

  24. Conclusion • Visual AntClust is able to treat from little to important data sets • It performs well and even better than k-Means initialised with the expected number of clusters for some data sets • Perspectives: finding automatically the best parameters setting • www.antsearch.univ-tours.fr

  25. The End

  26. Other ant-based clustering algorithms (2/3) 1st group objects 2nd group objects Artificial ants Rs

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