Abstract
OPTICS is a popular tool to analyze the clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based clustering specified by the height as the density threshold for clusters. However, in very dynamic and rapid changing applications a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial cluster instances. The offline phase reveals the current data clustering structure with low complexity and at any time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: ordering points to identify the clustering structure. In: ACM SIGMOD Record, vol. 28, no. 2, pp. 49–60. ACM (1999)
Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: Proceedings of the 2006 SIAM International Conference on Data Mining, pp. 328–339. SIAM (2006)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, no. 34, pp. 226–231 (1996)
Hinneburg, A., Keim, D.A., et al.: An efficient approach to clustering in large multimedia databases with noise. In: KDD, vol. 98, pp. 58–65 (1998)
McInnes, L., Healy, J., Astels, S.: HDBSCAN: hierarchical density based clustering. J. Open Source Softw. 2(11), 205 (2017)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vision 40(2), 99–121 (2000)
Sander, J., Ester, M., Kriegel, H.P., Xu, X.: Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min. Knowl. Disc. 2(2), 169–194 (1998)
Tasoulis, D.K., Ross, G., Adams, N.M.: Visualising the cluster structure of data streams. In: R. Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds.) IDA 2007. LNCS, vol. 4723, pp. 81–92. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74825-0_8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Richter, F., Lu, Y., Kazempour, D., Seidl, T. (2020). AMTICS: Aligning Micro-clusters to Identify Cluster Structures. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12112. Springer, Cham. https://doi.org/10.1007/978-3-030-59410-7_52
Download citation
DOI: https://doi.org/10.1007/978-3-030-59410-7_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59409-1
Online ISBN: 978-3-030-59410-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)