{"type":"ScholarlyArticle","title":"Swarm Intelligence for Self-Organized Clustering (Extended Abstract)","authors":[{"name":"Michael C. Thrun","slug":"michael-c-thrun","given":"Michael C.","family":"Thrun","affiliation":"Databionics Research Group , Philipps-University of Marburg , Germany"},{"name":"Alfred Ultsch","slug":"alfred-ultsch","given":"Alfred","family":"Ultsch","affiliation":"Databionics Research Group , Philipps-University of Marburg , Germany"}],"abstract":"The Databionic swarm (DBS) is a flexible and robust clustering framework that consists of three independent modules: swarm based projection, high-dimensional data visualization and representation guided clustering.  The first module is the parameter-free projection method Pswarm, which exploits concepts of self-organization and emergence, game theory, and swarm intelligence. The second module is a parameter-free high-dimensional data visualization technique called topographic map. It uses the generalized U-matrix, which enables to estimate first, if any cluster tendency exists and second, the estimation of the number of clusters. The third module offers a clustering method which can be verified by the visualization and vice versa. Benchmarking w.r.t. conventional algorithms demonstrated that DBS can outperform them. Several applications showed that cluster structures provided by DBS are meaningful. Exemplary, a clustering of worldwide country-related data w.r.t the COVID-19 pandemic is presented here. Code and data is made available via open source.","doi":"10.24963/ijcai.2020/720","url":"https://ijcai.org/proceedings/2020/720","pdf":"https://ijcai.org/proceedings/2020/0720.pdf","markdown":"https://ijcai.org/proceedings/2020/720.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/720","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Journal track","track":"Journal track","number":720,"pages":{"first":5125,"last":5129},"keywords":["Agent-based and Multi-agent Systems: Noncooperative Games","Agent-based and Multi-agent Systems: Multi-agent Learning","Computer Vision: Statistical Methods and Machine Learning","Data Mining: Clustering, Unsupervised Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24537/file_24537.m3u8","length_sec":266,"provider":"hls"}],"references":[{"position":1,"raw":"[Adolfsson et al., 2019] Andreas Adolfsson, Margareta Ack- erman, and Naomi C Brownstein. To cluster, or not to clus- ter: An analysis of clusterability methods. Pattern Recog- nition, 88:13-26, 2019.","title":"To cluster, or not to cluster: An analysis of clusterability methods","authors":"Adolfsson","year":"2019","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bonner, 1964] R. E. Bonner. On some clustering technique. IBM Journal of Research and Development, 8(1):22-32, 1964.","title":"On some clustering technique","authors":"R E Bonner","year":"1964","venue":"IBM Journal of Research and Development","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Charrad et al., 2012] Malika Charrad, Nadia Ghazzali, Véronique Boiteau, and Azam Niknafs. Nbclust package: finding the relevant number of clusters in a dataset. Jour- nal of Statistical Software, 61(6):1-36, 2012.","title":"Nbclust package: finding the relevant number of clusters in a dataset","authors":"Malika Charrad, Nadia Ghazzali, Véronique Boiteau, Azam Niknafs","year":"2012","venue":"Journal of Statistical Software","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dasgupta and Gupta, 2003] Sanjoy Dasgupta and Anupam Gupta. An elementary proof of a theorem of johnson and lindenstrauss. Random Structures & Algorithms, 22(1):60-65, 2003.","title":"An elementary proof of a theorem of johnson and lindenstrauss","authors":"Sanjoy Dasgupta, Anupam Gupta","year":"2003","venue":"Random Structures & Algorithms","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Dijkstra, 1959] Edsger W Dijkstra. A note on two prob- lems in connexion with graphs. Numerische Mathematik, 1(1):269-271, 1959.","title":"A note on two problems in connexion with graphs","authors":"W Edsger, Dijkstra","year":"1959","venue":"Numerische Mathematik","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Duda et al., 2001] Richard O Duda, Peter E Hart, and David G Stork. Pattern Classification. A Wiley- Interscience Publication. John Wiley & Sons, Ney York, USA, second edition, 2001.","title":"Pattern Classification","authors":"Peter E Richard O Duda, David G Hart, Stork","year":"2001","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Everitt et al., 2001] Brian S Everitt, Sabine Landau, and Morven Leese. Cluster Analysis. Arnold, London, fourth edition, 2001.","title":"Cluster Analysis","authors":"Brian S Everitt, Sabine Landau, Morven Leese","year":"2001","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Fayyad et al., 1996] Usama M Fayyad, Gregory Piatetsky- Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy. Advances in knowledge discovery and data mining, vol- ume 21. American Association for Artificial Intelligence press, Menlo Park, California, USA, 1996.","title":"Advances in knowledge discovery and data mining","authors":"Gregory Usama M Fayyad, Padhraic Piatetsky-Shapiro, Ramasamy Smyth, Uthurusamy","year":"1996","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Geman et al., 1992] Stuart Geman, Elie Bienenstock, and René Doursat. Neural networks and the bias/variance dilemma. Neural Computation, 4(1):1-58, 1992.","title":"Neural networks and the bias/variance dilemma","authors":"Stuart Geman, Elie Bienenstock, René Doursat","year":"1992","venue":"Neural Computation","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Goldstein, 1999] Jeffrey Goldstein. Emergence as a con- struct: History and issues. Emergence, 1(1):49-72, 1999.","title":"Emergence as a construct: History and issues","authors":"Jeffrey Goldstein","year":"1999","venue":"Emergence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Handl et al., 2005] Julia Handl, Joshua Knowles, and Dou- glas B Kell. Computational cluster validation in post- genomic data analysis. Bioinformatics, 21(15):3201- 3212, 2005.","title":"Computational cluster validation in postgenomic data analysis","authors":"Julia Handl, Joshua Knowles, Douglas B Kell","year":"2005","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hennig, 2015] et al. (eds.) Hennig, Christian. Handbook of cluster analysis. Handbook of Modern Statistical Meth- ods. Chapman & Hall/CRC Press, New York, USA, 2015.","title":"Hennig, Christian. Handbook of cluster analysis. Handbook of Modern Statistical Methods","authors":"Hennig","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lötsch and Ultsch, 2014] Jörn Lötsch and Alfred Ultsch. Exploiting the structures of the u-matrix. In Th. Vill- mann, F.-M. Schleif, M. Kaden, and M. Lange, editors, Advances in Self-Organizing Maps and Learning Vector Quantization, pages 249-257. Springer International Pub- lishing, June 2014.","title":"Exploiting the structures of the u-matrix","authors":"Ultsch Lötsch, Jörn Lötsch, Alfred Ultsch","year":"2014","venue":"Advances in Self-Organizing Maps and Learning Vector Quantization","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Martens et al., 2011] David Martens, Bart Baesens, and Tom Fawcett. Editorial survey: swarm intelligence for data mining. Machine Learning, 82(1):1-42, 2011.","title":"Editorial survey: swarm intelligence for data mining","authors":"David Martens, Bart Baesens, Tom Fawcett","year":"2011","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mirkin, 2005] Boris G. Mirkin. Clustering: a data recovery approach. Computer Science and Data Analysis Series. Chapnman & Hall/CRC, Boca Raton, FL, USA, 2005.","title":"Clustering: a data recovery approach","authors":"Boris G Mirkin","year":"2005","venue":"Computer Science and Data Analysis Series","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Murtagh, 2004] Fionn Murtagh. On ultrametricity, data coding, and computation. Journal of Classification, 21(2):167-184, 2004.","title":"On ultrametricity, data coding, and computation","authors":"Fionn Murtagh","year":"2004","venue":"Journal of Classification","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Nash, 1950] John Forbes Nash. Equilibrium points in n- person games. Proc. Nat. Acad. Sci. USA, 36(1):48-49, 1950.","title":"Equilibrium points in nperson games","authors":"John Forbes, Nash","year":"1950","venue":"Proc. Nat. Acad. Sci","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Nash, 1951] John Forbes Nash. Non-cooperative games. Annals of Mathematics, pages 286-295, 1951.","title":"Non-cooperative games","authors":"John Forbes, Nash","year":"1951","venue":"Annals of Mathematics","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ritter, 2014] Gunter Ritter. Robust cluster analysis and variable selection. Monographs on Statistics and Applied Probability. Chapman & Hall/CRC Press, Passau, Ger- many, 2014.","title":"Robust cluster analysis and variable selection","authors":"Gunter Ritter","year":"2014","venue":"Monographs on Statistics and Applied Probability","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Theodoridis and Koutroumbas, 2009] Sergios Theodoridis and Konstantinos Koutroumbas. Pattern Recognition. El- sevier, Canada, fourth edition, 2009.","title":"Sergios Theodoridis and Konstantinos Koutroumbas. Pattern Recognition","authors":"Koutroumbas Theodoridis","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Thrun and Ultsch, 2018] Michael C. Thrun and Alfred Ultsch. Effects of the payout system of income taxes to municipalities in germany. In Monika Papież and Sławomir Śmiech, editors, 12th Professor Aleksander Zelias International Conference on Modelling and Fore- casting of Socio-Economic Phenomena, pages 533-542, May 2018.","title":"Effects of the payout system of income taxes to municipalities in germany","authors":"C Michael, Alfred Thrun, Ultsch","year":"2018","venue":"Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Thrun and Ultsch, 2020] Michael C. Thrun and Alfred Ultsch. Clustering benchmark datasets exploiting the fundamental clustering problems. Data in Brief, 30(C):105501, 2020.","title":"Clustering benchmark datasets exploiting the fundamental clustering problems","authors":"C Michael, Alfred Thrun, Ultsch","year":"2020","venue":"Data in Brief","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Thrun et al., 2016] Michael C. Thrun, F Lerch, Jörn Lötsch, and Alfred Ultsch. Visualization and 3d printing of mul- tivariate data of biomarkers. In Vaclav Scala, editor, In- ternational Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), volume 24, pages 7-16, May 2016.","title":"Visualization and 3d printing of multivariate data of biomarkers","authors":"C Michael, Thrun, Jörn Lerch, Alfred Lötsch, Ultsch","year":"2016","venue":"ternational Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG)","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Thrun, 2018] Michael C. Thrun. Projection Based Clus- tering through Self-Organization and Swarm Intelligence. Springer, Heidelberg, 2018.","title":"Projection Based Clustering through Self-Organization and Swarm Intelligence","authors":"C Michael, Thrun","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Thrun, 2020] Michael C. Thrun. Improving the sensitiv- ity of statistical testing for clusterability with mirrored- density plot. In Ian Archambault Daniel, Nabney and Pel- tonen Jaakkor, editors, Machine Learning Methods in Vi- sualisation for Big Data (MLVis), pages 1-5. The Euro- graphics Association, May 2020.","title":"Improving the sensitivity of statistical testing for clusterability with mirroreddensity plot","authors":"C Michael, Thrun","year":"2020","venue":"Machine Learning Methods in Visualisation for Big Data (MLVis)","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Ultsch and Lötsch, 2017] Alfred Ultsch and Jörn Lötsch. Machine-learned cluster identification in high- dimensional data. Journal of Biomedical Informatics, 66(C):95-104, 2017.","title":"Machine-learned cluster identification in highdimensional data","authors":"Lötsch Ultsch, Alfred Ultsch, Jörn Lötsch","year":"2017","venue":"Journal of Biomedical Informatics","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Ultsch and Thrun, 2017] Alfred Ultsch and Michael C. Thrun. Credible visualizations for planar projections. In Marie Cottrell, editor, 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM), pages 1-5, June 2017.","title":"Credible visualizations for planar projections","authors":"Thrun Ultsch, Alfred Ultsch, Michael C Thrun","year":"2017","venue":"12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Ultsch, 1999] Alfred Ultsch. Data mining and knowledge discovery with emergent self-organizing feature maps for multivariate time series, pages 33-46. Elsevier, first edi- tion, 1999.","title":"Alfred Ultsch. Data mining and knowledge discovery with emergent self-organizing feature maps for multivariate time series","authors":"Ultsch","year":"1999","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Ultsch, 2000] Alfred Ultsch. Clustering with databots. In nt. Conf. Advances in Intelligent Systems Theory and Ap- plications (AISTA), pages 99-104, Feb 2000.","title":"Clustering with databots","authors":"Alfred Ultsch, Ultsch","year":"2000","venue":"nt. Conf. Advances in Intelligent Systems Theory and Applications (AISTA)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}