Εργαστήριο Γνώσης και Αβεβαιότητας

Knowledge and Uncertainty Research Laboratory

Robust, Generalized, Quick and Efficient Agglomerative Clustering

[conference]


Full reference

M. Wallace, S. Kollias, Robust, Generalized, Quick and Efficient Agglomerative Clustering, Proceedings of 6th International Conference on Enterprise Information Systems, Porto, Portugal, April 2004


Abstract

Hierarchical approaches, which are dominated by the generic agglomerative clustering algorithm, are suitable for cases in which the count of distinct clusters in the data is not known a priori; this is not a rare case in real data. On the other hand, important problems are related to their application, such as susceptibility to errors in the initial steps that propagate all the way to the final output and high complexity. Finally, similarly to all other clustering techniques, their efficiency decreases as the dimensionality of their input increases. In this paper we propose a robust, generalized, quick and efficient extension to the generic agglomerative clustering process. Robust refers to the proposed approachs ability to overcome the classic algorithms susceptibility to errors in the initial steps, generalized to its ability to simultaneously consider multiple distance metrics, quick to its suitability for application to larger datasets via the application of the computationally expensive components to only a subset of the available data samples and efficient to its ability to produce results that are comparable to those of trained classifiers, largely outperforming the generic agglomerative process.


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7 known citations

  1. G. Antonini and J. Thiran, Counting pedestrians in video sequences using trajectory clustering, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 16, Nr. 8, pp. 1008 - 1020, 2006
  2. Gianluca Antonini, A discrete choice modeling framework for pedestrian walking behavior with application to human tracking in video sequences, Thesis, EPFL, 2005.
  3. Omid Khayat and Hamid Reza Shahdoosti, Shahyat Algorithm as a Clustering Method, Proceedings Of The European Computing Conference, 151-160, 2009
  4. Gianluca Antonini, Michel Bierlaire, Mats Weber, Discrete choice models of pedestrian walking behavior, Transportation Research Part B: Methodological, Volume 40, Issue 8, September 2006, Pages 667-687
  5. Syrris, Vassilis and Petridis, Vassilios. Classification through hierarchical clustering and dimensionality reduction. IJCNN, 2008.
  6. Masahiro Iwasaki, Kunio Nobori, Moving object detection apparatus and moving object detection method. Patent 20110091073 A1. 2013.
  7. Karuna Katariya and Rajanikanth Aluvalu. Article: Agglomerative Clustering in Web Usage Mining: A Survey. International Journal of Computer Applications 89(8):24-27, March 2014.