Abstract
There is much recent discussion on data streams and big data, which except of their volume and velocity are also characterized by volatility. Next to detecting change, it is also important to interpret it. Consider customer profiling as an example: Is a cluster corresponding to a group of customers simply disappearing or are its members migrating to other clusters? Does a new cluster reflect a new type of customers or does it rather consist of old customers whose preferences shift? To answer such questions, we have proposed the framework MONIC [20] for modeling and tracking cluster transitions. MONIC has been re-discovered some years after publication and is enjoying a large citation record from papers on community evolution, cluster evolution, change prediction and topic evolution.
Chapter PDF
Similar content being viewed by others
References
Aung, H.H., Tan, K.-L.: Discovery of evolving convoys. In: SSDBM, pp. 196–213 (2010)
Bifet, A., Holmes, G., Pfahringer, B., Kranen, P., Kremer, H., Jansen, T., Seidl, T.: Moa: Massive online analysis, a framework for stream classification and clustering. Journal of Machine Learning Research - Proceedings Track 11, 44–50 (2010)
Böttcher, M., Höppner, F., Spiliopoulou, M.: On exploiting the power of time in data mining. SIGKDD Explorations 10(2), 3–11 (2008)
Falkowski, T., Bartelheimer, J., Spiliopoulou, M.: Mining and visualizing the evolution of subgroups in social networks. In: Web Intelligence, pp. 52–58 (2006)
Gohr, A., Hinneburg, A., Schult, R., Spiliopoulou, M.: Topic evolution in a stream of documents. In: SDM (2009)
Günnemann, S., Kremer, H., Laufkötter, C., Seidl, T.: Tracing evolving clusters by subspace and value similarity. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 444–456. Springer, Heidelberg (2011)
Hahsler, M., Dunham, M.H.: Temporal structure learning for clustering massive data streams in real-time. In: SDM, pp. 664–675 (2011)
He, Q., Chen, B., Pei, J., Qiu, B., Mitra, P., Giles, C.L.: Detecting topic evolution in scientific literature: how can citations help? In: CIKM, pp. 957–966 (2009)
Jensen, C.S., Lin, D., Ooi, B.C.: Continuous clustering of moving objects. TKDE 19(9), 1161–1174 (2007)
Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of convoys in trajectory databases. CoRR, abs/1002.0963 (2010)
Jo, Y., Hopcroft, J.E., Lagoze, C.: The web of topics: discovering the topology of topic evolution in a corpus. In: WWW, pp. 257–266 (2011)
Lauschke, C., Ntoutsi, E.: Monitoring user evolution in twitter. In: BASNA Workshop, co-located with ASONAM (2012)
Ntoutsi, E., Mitsou, N., Marketos, G.: Traffic mining in a road-network: How does the traffic flow? IJBIDM 3(1), 82–98 (2008)
Ntoutsi, E., Spiliopoulou, M., Theodoridis, Y.: Tracing cluster transitions for different cluster types. Control & Cybernetics Journal 38(1), 239–260 (2009)
Ntoutsi, I., Spiliopoulou, M., Theodoridis, Y.: Summarizing cluster evolution in dynamic environments. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part II. LNCS, vol. 6783, pp. 562–577. Springer, Heidelberg (2011)
Ntoutsi, E., Spiliopoulou, M., Theodoridis, Y.: FINGERPRINT summarizing cluster evolution in dynamic environments. IJDWM (2012)
Oliveira, M.D.B., Gama, J.: A framework to monitor clusters evolution applied to economy and finance problems. Intell. Data Anal. 16(1), 93–111 (2012)
Spiliopoulou, M.: Evolution in social networks: A survey. In: Aggarwal, C.C. (ed.) Social Network Data Analytics, pp. 149–175. Springer (2011)
Spiliopoulou, M., Ntoutsi, E., Theodoridis, Y.: Tracing cluster transitions for different cluster types. In: ADMKD Workshop, co-located with ADBIS (2007)
Spiliopoulou, M., Ntoutsi, E., Theodoridis, Y., Schult, R.: MONIC – modeling and monitoring cluster transitions. In: KDD, pp. 706–711 (2006)
Tantipathananandh, C., Berger-Wolf, T.Y.: Finding communities in dynamic social networks. In: ICDM, pp. 1236–1241 (2011)
Vieira, M.R., Bakalov, P., Tsotras, V.J.: On-line discovery of flock patterns in spatio-temporal data. In: GIS, pp. 286–295 (2009)
Zimmermann, M., Ntoutsi, E., Siddiqui, Z.F., Spiliopoulou, M., Kriegel, H.-P.: Discovering global and local bursts in a stream of news. In: SAC, pp. 807–812 (2012)
Zimmermann, M., Ntoutsi, E., Spiliopoulou, M.: Extracting opinionated (sub)features from a stream of product reviews. In: DS (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Spiliopoulou, M., Ntoutsi, E., Theodoridis, Y., Schult, R. (2013). MONIC and Followups on Modeling and Monitoring Cluster Transitions. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science(), vol 8190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40994-3_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-40994-3_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40993-6
Online ISBN: 978-3-642-40994-3
eBook Packages: Computer ScienceComputer Science (R0)