BOCK, H.H. (1996): Probabilistic models in cluster analysis. Computational Statistics & Data Analysis, 23, 5-28. Home/Search Document Not in Database Summary Related Articles Check |
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Probability Models for Connected Operators - Gatica-Perez, Sun, Gu (2002) (Correct)
....exceptions [18] connected operators have been formulated in 2 DANIEL GATICA PEREZ et al. deterministic terms. In this paper, we argue that the introduction of probability models in the design of connected operators is useful both to generalize existing formulations and to define new designs [1] [3], 6] In particular, we introduce an operator based on the formulation of hierarchical clustering as a sequential binary classification process, and on the development of statistical models of visual similarity among image regions. Its performance is illustrated on image collections and video ....
....alternative to encode the a priori knowledge of the problem consists on the use of probability models [6] 3. A probabilistic view of attribute based connected operators Hierarchical agglomerative clustering algorithms based on probability models are increasingly used in pattern recognition [1] [3], 6] The simplest algorithm Fig. 1. a) Giraffe. b c) Filtered images with area connected operator. All components with area less than (b) 100 and (c) 1000 pixels, are removed. The merging order depends on both area (minimum size) and intensity (color difference) attributes. consists of a ....
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H.H. Bock, "Probabilistic Models in Cluster Analysis," Computational Statistics and Data Analysis, Vol. 23, pp. 5-28.
Clustering Techniques: A Brief Survey - Hösel, Walcher (2001) (Correct)
....2 T; i; j 2 C) denoting the heterogeneity h(C) of the smallest class C which contains both objects I i and I j . This dissimilarity measure d i;j satis es the ultrametric inequality d i;j max( d i;k ; d k;j ) for all objects I i ; I j ; I k and is thus also a metric (compare [4]) The various hierarchical clustering methods di er in how they derive class distances from the dissimilarities of the objects. A study of the continuity or pseudocontinuity of this assignment for di erent clustering procedures can be found in [22] Two major concepts exist to implement ....
....of the problem changes fundamentally in such a framework. A large amount of recent research has been devoted to statistical models and tests to validate cluster structures and homogeneity of groups. A survey on cluster analysis in such an probabilistic and inferential framework is provided by [4]. The paper discusses (among other methods) partition type models for dissimilarities and random graphs. It presents Markovian branching processes as well as phylogenetic inference based on molecular data. A comparison of the mixture and the classi cation maximum likelihood for binary data is ....
H.H. Bock, Probabilistic models in cluster analysis. Computational Statistics & Data Analysis 23, 5 - 28 (1996).
Bagged Clustering - Leisch (1999) (1 citation) (Correct)
....directly from these de nitions that d s ful lls both (I) and (II) while d w is only (I) Suppose that F is absolute continuous on the input space X such that the density f exists. One possible de nition of a cluster is to assume that f is multimodal with each modus corresponding to one cluster [17]. The following definition characterizes a clustering problem by the amount of background noise (maximum density outside the clusters) and the minimum distance between the clusters. De nition 1: We call the pairwise disjoint sets A i X , i = 1; M , separated clusters with ....
H. H. Bock, \Probabilistic models in cluster analysis," Computational Statistics & Data Analysis, vol. 23, pp. 5-28, 1996.
Evidence for a Relationship Between Algorithmic Scheme - And Shape Of (Correct)
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BOCK, H.H. (1996): Probabilistic models in cluster analysis. Computational Statistics & Data Analysis, 23, 5-28.
Robust Clustering under General Normal Assumptions - Gallegos (Correct)
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Bock, H.H., 1996. Probabilistic models in cluster analysis. Computational Statistics and Data Analysis 23, 5--28.
Unsupervised Pattern Recognition - Dimensionality Reduction and.. - De Backer (2002) (Correct)
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H. H. Bock. Probabilistic models in cluster analysis. Computational Statistics & Data Analysis, 23, 1996.
Estimation and Prediction for Stochastic Blockstructures - Nowicki, Snijders (Correct)
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BOCK, H.H. (1996a), "Probabilistic models in cluster analysis", Computational Statistics and Data Analysis, 23, 5 -- 28.
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