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Visual Clustering with Artificial Ant Colonies: Innovative Algorithms and Results

This paper explores the development of Visual AntClust, an ant-based unsupervised clustering tool inspired by the chemical recognition mechanisms of real ants. By modeling how ants identify and group nestmates, we extracted core principles to create an effective clustering algorithm. The study compares Visual AntClust with traditional clustering methods like K-Means, highlighting its ability to handle various datasets and outperform established techniques in several cases. We discuss the algorithm's structure, experimentation, and potential for enhancing data clustering processes.

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Visual Clustering with Artificial Ant Colonies: Innovative Algorithms and Results

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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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