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Bayesian hierarchical clustering
Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages over traditional distance-based agglomerative clustering algorithms. (1) It defines a probabilistic model of the data which can be used to compute the predictive distribution of ...
Katherine A. Heller, Zoubin Ghahramani
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Hierarchical Ensemble Clustering
2010 IEEE International Conference on Data Mining, 2010Ensemble clustering has emerged as an important elaboration of the classical clustering problems. Ensemble clustering refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better fit in some sense than the existing ...
Li Zheng 0001 +2 more
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Hierarchical Clustering Schemes
Psychometrika, 1967Techniques for partitioning objects into optimally homogeneous groups on the basis of empirical measures of similarity among those objects have received increasing attention in several different fields. This paper develops a useful correspondence between any hierarchical system of such clusters, and a particular type of distance measure.
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IEEE 6th Workshop on Multimedia Signal Processing, 2004., 2005
We present a novel generative model for video that models video as mixture of transformed video scenes. The learning procedure automatically clusters video frames into video scenes and objects. The learning algorithm is based on a hierarchical, on-line EM algorithm.
Nemanja Petrovic +2 more
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We present a novel generative model for video that models video as mixture of transformed video scenes. The learning procedure automatically clusters video frames into video scenes and objects. The learning algorithm is based on a hierarchical, on-line EM algorithm.
Nemanja Petrovic +2 more
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Introduction to Hierarchical Clustering
Journal of Clinical Neurophysiology, 2002Hierarchical clustering of spike events is a method of grouping events that are similar in topology, morphology, or both, and it provides a method of efficient, detailed analysis of interictal events. Information about the relative populations of spikes at multiple foci is presented, and artifact events are grouped and eliminated en masse.
Michael J, Guess, Scott B, Wilson
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Stability of a hierarchical clustering
Pattern Recognition, 1980Abstract Clustering algorithms have the annoying habit of finding clusters even when the data are generated randomly. Verifying that potential clusterings are real in some objective sense is receiving more attention as the number of new clustering algorithms and their applications grow.
Stephen P. Smith, Richard C. Dubes
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Compressed Hierarchical Clustering
2018 Data Compression Conference, 2018Hierarchical Clustering is widely used in Machine Learning and Data Mining. It stores bit-vectors in the nodes of a k-ary tree, usually without trying to compress them. We suggest a double usage of the {\sf xor}ing operations defining the Hamming distance used in the clustering process, extending it also to be used to transform the vector in one node ...
Gilad Baruch +2 more
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On Hierarchical Clustering of Spectrogram
2018We propose a new method of applying Generative Theory of Tonal Music directly to a spectrogram of music to produce a time-span segmentation as hierarchical clustering. We first consider a vertically long rectangle in a spectrogram (bin) as a pitch event and a spectrogram as a sequence of bins.
Shun Sawada +2 more
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On-line hierarchical clustering
Pattern Recognition Letters, 1998Summary: Most of the techniques used in the literature for hierarchical clustering are based on off-line operation. The main contribution of this paper is to propose a new algorithm for on-line hierarchical clustering by finding the nearest \(k\) objects to each introduced object so far and these nearest \(k\) objects are continuously updated by the ...
Yasser El-Sonbaty, Mohamed A. Ismail
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Non-hierarchical clustering with masloc
Pattern Recognition, 1983Abstract MASLOC is a cluster analysis technique based on the p-median model of location theory, which permits for each value of p, clustering the given objects into p groups, each provided with a representative object. Both an exact and heuristic method are described, along with their application range.
Massart, Desire +2 more
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