Towards Improving Clustering Ants: An Adaptive Ant Clustering Algorithm

Among the many bio-inspired techniques, ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key features that would make such algorithms mature tools for diverse applications. Under this perspective, this paper proposes both a progressive vision scheme and pheromone heuristics for the standard ant-clustering algorithm, together with a cooling schedule that improves its convergence properties. The proposed algorithm is evaluated in a number of well-known benchmark data sets, as well as in a real-world bio informatics dataset. The achieved results are compared to those obtained by the standard ant clustering algorithm, showing that significant improvements are obtained by means of the proposed modifications. As an additional contribution, this work also provides a brief review of ant-based clustering algorithms.292143154Abraham, A., Ramos, V., Web usage mining using artificial ant colony clustering and genetic programming (2003) Proc. of the Congress on Evolutionary Computation (CEC 2003), pp. 1384-1391. , Canberra, IEEE PressBezdek, J.C., (1981) Pattern Recognition with Fuzzy Objective Function Algorithm, , Plenum PressBonabeau, E., Dorigo, M., Théraulaz, G., (1999) Swarm Intelligence from Natural to Artificial Systems, , Oxford University PressCamazine, S., Deneubourg, J.-L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E., (2001) Self-organization in Biological Systems, , Princeton University PressDe Castro, L.N., Von Zuben, F.J., (2004) Recent Developments in Biologically Inspired Computing, , Idea Group IncDeneubourg, J.L., Goss, S., Sendova-Franks, N.A., Detrain, C., Chrétien, L., The dynamics of collective sorting: Robot-like ant and ant-like robot (1991) Simulation of Adaptive Behavior: from Animals to Animats, pp. 356-365. , J. 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Conf. on Parallel Problem Solving from Nature, Granada, Spain, Springer-Verlag, BerlinKanade, P., Hall, L.O., Fuzzy ants as a clustering concept (2003) Proc. of the 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 227-232Kaufman, L., Rousseeuw, P.J., (1990) Finding Groups in Data - An Introduction to Cluster Analysis, Wiley Series in Probability and Mathematical Statistics, , John Wiley & Sons IncKeim, D.A., (2002) Information Visualization and Visual Data Mining: IEEE Transactions on Visuali Zation and Computer Graphics, 7 (1), pp. 100-107Kennedy, J., Eberhart, R., Shi, Y., (2001) Swarm Intelligence, , Morgan Kaufmann PublishersLabroche, N., Monmarché, N., Venturini, G., A new clustering algorithm based on the chemical recognition system of ants (2002) Proc. of the 15th European Conference on Artificial Intelligence, pp. 345-349. , France, IOS PressLumer, E.D., Faieta, B., Diversity and adaptation in populations of clustering ants (1994) Proceedings of the Third International Conference on the Simulation of Adaptive Behavior: from Animals to Animats, 3, pp. 499-508. , MIT PressMonmarché, N., Slimane, M., Venturini, G., On improving clustering in numerical databases with artificial ants. Advances in artificial life (1999) Lecture Notes in Computer Science, 1674, pp. 626-635. , D. Floreano, J.D. Nicoud, and F. Mondala Eds., Springer-Verlag, BerlinPaton, R., (1994) Computing with Biological Metaphors, , Chapman & HallRamos, V., Merelo, J.J., Self-organized stigmergic document maps: Environment as a mechanism for context learning (2002) AEB'2002, First Spanish Conference on Evolutionary and BioInspired Algorithms, pp. 284-293. , E. Alba, F. Herrera, J.J. Merelo et al. Eds., SpainRamos, V., Muge, F., Pina, P., Self-organized data and image retrieval as a consequence of inter-dynamic synergistic relationships in artificial ant colonies (2002) Soft-Computing Systems - Design, Management and Applications, Frontiers in Artificial Intelligence and Applications, 87, pp. 500-509. , J. Ruiz-del-Solar, A. Abrahan and M. Köppen Eds. IOS Press, AmsterdamRitter, H., Kohonen, T., Self-organizing semantic maps (1989) Biol. Cybern., 61, pp. 241-254Sherafat, V., De Castro, L.N., Hruschka, E.R., TermitAnt: An ant clustering algorithm improved by ideas from termite colonies (2004) Lecture Notes in Computer Science, 3316, pp. 1088-1093. , Proc. of ICONIP 2004, Special Session on Ant Colony and Multi-Agent SystemsSherafat, V., De Castro, L.N., Hruschka, E.R., The influence of pheromone and adaptive vision on the standard ant clustering algorithm (2004) Recent Developments in Biologically Inspired Computing, pp. 207-234. , L. N. de Castro and F. J. Von Zuben, Chapter IX. Idea Group IncVizine, A.L., De Castro, L.N., Gudwin, R.R., Text document classification using swarm intelligence (2005) Proc. of KIMAS 2005, , CD ROMYeung, K.Y., Medvedovic, M., Bumgarner, R.E., Clustering gene-expression data with repeated measurements (2003) Genome Biology, 4 (5), pp. R34. , articl

A fuzzy extension of the silhouette width criterion for cluster analysis

The present paper proposes a new cluster validity measure as an additional criterion to help the decision making process in fuzzy cluster analysis. This measure, named Fuzzy Silhouette, is a generalization to the fuzzy case of the Average Silhouette Width Criterion, originally conceived to assess crisp (non-fuzzy) data partitions. The Fuzzy Silhouette is more appealing than its crisp counterpart in the context of fuzzy cluster analysis since it makes explicit use of the fuzzy partition matrix provided by the clustering algorithm. In addition, it has been designed to improve performance of the original silhouette criterion in detecting regions with higher data density when the data set involves overlapping clusters. The performance of the Fuzzy Silhouette is evaluated and compared to that of five well-known cluster validity measures. Six data sets are used to illustrate different scenarios in which the proposed Fuzzy Silhouette performs similar to or better than these other criteria, thus becoming eligible to join a pool of measures to be used all together in fuzzy cluster analysis

Fuzzy clustering-based filter

This paper introduces a filter, named FCF (Fuzzy Clustering-based Filter), for removing redundant features, thus making it possible to improve the efficacy and the efficiency of data mining algorithms. FCF is based on the fuzzy partitioning of features into clusters. The number of clusters is automatically estimated from data. After the clustering process, FCF selects a subset of features from the obtained clusters. To do so, we study four different strategies that are based on the information provided by the fuzzy partition matrix. We also show that these strategies can be combined for better performance. Empirical results illustrate the performance of FCF, which in general has obtained competitive results in classification tasks when compared to a related filter that is based on the hard partitioning of features

Termitant: An Ant Clustering Algorithm Improved By Ideas From Termite Colonies

This paper proposes a heuristic to improve the convergence speed of the standard ant clustering algorithm. The heuristic is based on the behavior of termites that, when building their nests, add some pheromone to the objects they carry. In this context, pheromone allows artificial ants to get more information, at the local level, about the work in progress at the global level. A sensitivity analysis of the algorithm is performed in relation to the proposed modification on a benchmark problem, leading to interesting results. © Springer-Verlag Berlin Heidelberg 2004.331610881093Bonabeau, E., Dorigo, M., Théraulaz, G., (1999) Swarm Intelligence from Natural to Artificial Systems, , Oxford University PressCamazine, S., Deneubourg, J.-L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E., (2001) Self-Organization in Biological Systems, , Princeton University PressDeneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L., The Dynamics of Collective Sorting: Robot-Like Ant and Ant-Like Robot (1991) Simulation of Adaptive Behavior: from Animals to Animals, pp. 356-365. , J. A. Meyer and S. W. Wilson (eds.) Cambridge, MA, MIT Press/Bradford BooksDorigo, M., (1992) Optimization, Learning and Natural Algorithms, (in Italian), , Ph.D. Thesis, Dipartimento di Elettronica, Politecnico di Milano, ITKennedy, J., Eberhart, R., Shi, Y., (2001) Swarm Intelligence, , Morgan Kaufmann PublishersKube, C.R., Parker, C.A.C., Wang, T., Zhang, H., Biologically Inspired Collective Robotics (2004) Recent Developments in Biologically Inspired Computing, , L. N. de Castro & F. J. Von Zuben, Idea Group Inc., Chapter 15Lumer, E.D., Faieta, B., Diversity and Adaptation in Populations of Clustering Ants (1994) Proc. of the 3 rd Int. Conf. on the Simulation of Adaptive Behavior: from Animals to Animats, 3, pp. 499-508. , D. Cliff, P. Husbands, J. A. Meyer, S.W. Wilson (eds.), MIT PressResnick, M., (1994) Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds, , Cambridge, MA: MIT PressRamos, V., Muge, F., Pina, P., Self-Organized Data and Image Retrieval as a Consequence of Inter-Dynamic Synergistic Relationships in Artificial Ant Colonies (2002) Soft-Computing Systems - Design, Management and Applications, Frontiers in Artificial Intelligence and Applications, 87, pp. 500-509. , J. Ruiz-del-Solar, A. Abrahan and M. Köppen Eds., IOS Press, Amsterda

Efficiency issues of evolutionary k-means

One of the top ten most influential data mining algorithms, k-means, is known for being simple and scalable. However, it is sensitive to initialization of prototypes and requires that the number of clusters be specified in advance. This paper shows that evolutionary techniques conceived to guide the application of k-means can be more computationally efficient than systematic (i.e., repetitive) approaches that try to get around the above-mentioned drawbacks by repeatedly running the algorithm from different configurations for the number of clusters and initial positions of prototypes. To do so, a modified version of a (k-means based) fast evolutionary algorithm for clustering is employed. Theoretical complexity analyses for the systematic and evolutionary algorithms under interest are provided. Computational experiments and statistical analyses of the results are presented for artificial and text mining data sets

Distributed fuzzy clustering with automatic detection of the number of clusters

We present a consensus-based algorithm to distributed fuzzy clustering that allows automatic estimation of the number of clusters. Also, a variant of the parallel Fuzzy c-Means algorithm that is capable of estimating the number of clusters is introduced. This variant, named DFCM, is applied for clustering data distributed across different data sites. DFCM makes use of a new, distributed version of the Xie-Beni validity criterion. Illustrative experiments show that for sites having data from different populations the developed consensus-based algorithm can provide better results than DFCM