Clustering Uncertain Data with Possible Worlds

Volk, Peter Benjamin; Rosenthal, Frank; Hahmann, Martin; Habich, Dirk; Lehner, Wolfgang

doi:10.1109/icde.2009.174

Cited by 19 publications

(17 citation statements)

References 20 publications

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“…Volk et al . [28] propose an approach based on the well‐known possible world scenario, where a clustering solution is derived from each possible world, and the various solutions are eventually aggregated to form a unique clustering by employing standard methods for clustering aggregation [29].…”

Section: Related Workmentioning

confidence: 99%

Generalized estimating equations for mixtures with varying concentrations

2013

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A finite mixture model is considered in which the mixing probabilities vary from observation to observation. A parametric model is assumed for one mixture component distribution, while the others are nonparametric nuisance parameters. Generalized estimating equations (GEE) are proposed for the semi‐parametric estimation. Asymptotic normality of the GEE estimates is demonstrated and the lower bound for their dispersion (asymptotic covariance) matrix is derived. An adaptive technique is developed to derive estimates with nearly optimal small dispersion. An application to the sociological analysis of voting results is discussed. The Canadian Journal of Statistics 41: 217–236; 2013 © 2013 Statistical Society of Canada

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Section: Related Workmentioning

confidence: 99%

Generalized estimating equations for mixtures with varying concentrations

2013

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“…Other recent works aim to address the high dimensionality in uncertain data by focusing on the problems of subspace clustering [26] and projected (or projective) clustering [27] over uncertain objects. Volk et al [28] propose an approach based on the well-known possible world scenario, where a clustering solution is derived from each possible world, and the various solutions are eventually aggregated to form a unique clustering by employing standard methods for clustering aggregation [29].…”

Section: Related Workmentioning

confidence: 99%

Minimizing the variance of cluster mixture models for clustering uncertain objects

Gullo

Ponti

Tagarelli

2012

Statistical Analysis

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In recent years, there has been a growing interest in clustering uncertain objects. In contrast to traditional, ‘sharp’ data representation models, uncertain objects are modeled as probability distributions defined over uncertainty regions. In this context, a major issue is related to the poor efficiency of existing algorithms, which is mainly due to expensive computation of the distance between uncertain objects. In this work, we extend our earlier work in which a novel formulation to the problem of clustering uncertain objects is defined based on the minimization of the variance of the mixture models that represent the clusters being discovered. Analytical properties about the computation of variance for cluster mixture models are derived and exploited by a partitional clustering algorithm, called MMVar. This algorithm achieves high efficiency since it does not need to employ any distance measure between uncertain objects. Experiments have shown that MMVar is scalable and outperforms state‐of‐the‐art algorithms in terms of efficiency, while achieving better average performance in terms of accuracy. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 6: 116–135, 2013

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“…In the data mining field, various mining techniques, such as clustering [9], [10], [11], [12], [13], [14] and frequent pattern mining techniques [15], [16], [17], are migrated from certain data to deal with uncertain data. In this section, we first review the models of uncertain data in Section VII-A, and then compare our work with the previous work on outlier detection on certain data and uncertain data, in Section VII-B and Section VII-C, respectively.…”

Section: Related Workmentioning

confidence: 99%

Outlier detection on uncertain data: Objects, instances, and inferences

Jiang

Pei

2011

2011 IEEE 27th International Conference on Data Engineering

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Abstract-This paper studies the problem of outlier detection on uncertain data. We start with a comprehensive model considering both uncertain objects and their instances. An uncertain object has some inherent attributes and consists of a set of instances which are modeled by a probability density distribution. We detect outliers at both the instance level and the object level. To detect outlier instances, it is a prerequisite to know normal instances. By assuming that uncertain objects with similar properties tend to have similar instances, we learn the normal instances for each uncertain object using the instances of objects with similar properties. Consequently, outlier instances can be detected by comparing against normal ones. Furthermore, we can detect outlier objects most of whose instances are outliers. Technically, we use a Bayesian inference algorithm to solve the problem, and develop an approximation algorithm and a filtering algorithm to speed up the computation. An extensive empirical study on both real data and synthetic data verifies the effectiveness and efficiency of our algorithms.

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Clustering Uncertain Data with Possible Worlds

Cited by 19 publications

References 20 publications

Generalized estimating equations for mixtures with varying concentrations

Generalized estimating equations for mixtures with varying concentrations

Minimizing the variance of cluster mixture models for clustering uncertain objects

Outlier detection on uncertain data: Objects, instances, and inferences

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