E. Achtert, H. P. Kriegel, and A. Zimek, ELKI: A Software System for Evaluation of Subspace Clustering Algorithms, Proceedings of the 20th international conference on Scientific and Statistical Database Management SSDBM '08, pp.580-585, 2008.
DOI : 10.1007/978-3-540-69497-7_41

P. Avgeriou and U. Zdun, Architectural patterns in practice, pp.731-734, 2005.

M. R. Berthold, N. Cebron, F. Dill, T. R. Gabriel, T. Ktter et al., KNIME: The Konstanz Information Miner, Data Analysis, Machine Learning and Applications Studies in Classification, Data Analysis, and Knowledge Organization, pp.319-326, 2008.
DOI : 10.1007/978-3-540-78246-9_38
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.177.2225

J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, 1981.
DOI : 10.1007/978-1-4757-0450-1

L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees, 1984.

S. Chekanov, Hep data analysis using jhepwork and java, Proceedings of the workshop HERA and the LHC, 2008.

S. Craß and E. Kühn, Coordination-based access control model for space-based computing, 27th Annual ACM Symposium on Applied Computing, 2012.

J. Dem?ar, B. Zupan, G. Leban, and T. Curk, Orange: From Experimental Machine Learning to Interactive Data Mining, Knowledge Discovery in Databases: PKDD 2004, pp.537-539, 2004.
DOI : 10.1007/978-3-540-30116-5_58

E. Denti and A. Omicini, An architecture for tuple-based coordination of multi-agent systems, Software: Practice and Experience, vol.8, issue.12, pp.1103-1121, 1999.
DOI : 10.1002/(SICI)1097-024X(199910)29:12<1103::AID-SPE273>3.0.CO;2-E

J. C. Dunn, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics, vol.3, issue.3, pp.32-57, 1973.
DOI : 10.1080/01969727308546046

K. C. Gowda and G. Krishna, Agglomerative clustering using the concept of mutual nearest neighbourhood, Pattern Recognition, vol.10, issue.2, pp.105-112, 1978.
DOI : 10.1016/0031-3203(78)90018-3

K. C. Gowda and T. V. Ravi, Divisive clustering of symbolic objects using the concepts of both similarity and dissimilarity, Pattern Recognition, vol.28, issue.8, pp.1277-1282, 1995.
DOI : 10.1016/0031-3203(95)00003-I

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann et al., The WEKA data mining software, ACM SIGKDD Explorations Newsletter, vol.11, issue.1, pp.10-18, 2009.
DOI : 10.1145/1656274.1656278

A. K. Jain, M. N. Murty, and P. J. Flynn, Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, pp.264-323, 1999.
DOI : 10.1145/331499.331504

K. Krishna and M. Narasimha-murty, Genetic K-means algorithm, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.29, issue.3, pp.433-439, 1999.
DOI : 10.1109/3477.764879

E. Kühn, R. Mordinyi, L. Keszthelyi, and C. Schreiber, Introducing the concept of customizable structured spaces for agent coordination in the production automation domain, The Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp.625-632, 2009.

E. Kühn and V. Sesum-cavic, A Space-Based Generic Pattern for Self-Initiative Load Balancing Agents, Lecture Notes in Computer Science, vol.5881, pp.17-32, 2009.
DOI : 10.1007/978-3-642-10203-5_3

R. J. Kuo, H. S. Wang, T. L. Hu, and S. H. Chou, Application of ant K-means on clustering analysis, Computers & Mathematics with Applications, vol.50, issue.10-12, pp.1709-1724, 2005.
DOI : 10.1016/j.camwa.2005.05.009

F. Mhamdi and M. Elloumi, A new survey on knowledge discovery and data mining, 2008 Second International Conference on Research Challenges in Information Science, pp.427-432, 2008.
DOI : 10.1109/RCIS.2008.4632134

I. Mierswa, M. Wurst, R. Klinkenberg, M. Scholz, and T. Euler, YALE, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.935-940, 2006.
DOI : 10.1145/1150402.1150531

F. E. Otero, A. A. Freitas, and C. G. Johnson, Handling continuous attributes in Ant Colony Classification algorithms, 2009 IEEE Symposium on Computational Intelligence and Data Mining, pp.225-231, 2009.
DOI : 10.1109/CIDM.2009.4938653
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.158.4957

R. Parpinelli, H. Lopes, and A. Freitas, Data mining with an ant colony optimization algorithm, IEEE Transactions on Evolutionary Computation, vol.6, issue.4, pp.321-332, 2002.
DOI : 10.1109/TEVC.2002.802452

T. N. Phyu and H. Kong, Survey of classification techniques in data mining In: Proceedings of the International MultiConference of Engineers and Computer Scientists, Lecture Notes in Engineering and Computer Science, International Association of Engineers, vol.I, issue.09, pp.727-731, 2009.

V. Sesum-cavic and E. Kühn, Chapter 8, self-organized load balancing through swarm intelligence Next Generation Data Technologies for Collective Computational Intelligence, Studies in Computational Intelligence, vol.352, pp.195-224, 2011.

P. S. Shelokar, V. K. Jayaraman, and B. D. Kulkarni, An ant colony approach for clustering, Analytica Chimica Acta, vol.509, issue.2, 2004.
DOI : 10.1016/j.aca.2003.12.032

S. Sonnenburg, G. Rätsch, S. Henschel, C. Widmer, J. Behr et al., The SHOGUN Machine Learning Toolbox, Journal of Machine Learning Research, 2010.

P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, 2005.

R. Tiwari, M. Husain, S. Gupta, and A. Srivastava, Improving ant colony optimization algorithm for data clustering, Proceedings of the International Conference and Workshop on Emerging Trends in Technology, ICWET '10, pp.529-534, 2010.
DOI : 10.1145/1741906.1742026

D. Weiss, A Clustering Interface For Web Search Results In Polish And English, 2001.