Active function Cross-Entropy Clustering

Spurek, Przemysław; Tabor, Jacek; Byrski, Krzysztof

doi:10.1016/j.eswa.2016.12.011

Cited by 13 publications

(12 citation statements)

References 47 publications

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“…Evaluation of possible dissimilarity metrics for categorical data can be found in dos Santos and Zárate (2015), Bai et al (2011). To obtain a more flexible structure of clusters, one can also use hierarchical methods (Zhao and Karypis 2002), density-based clustering (Wen et al 2002) or model-based techniques (Spurek 2017;Spurek et al 2017). One of important publicly available tools for efficient clustering of high dimensional binary data is the Cluto package (Karypis 2002).…”

Section: Distance-based Clusteringmentioning

confidence: 99%

Efficient mixture model for clustering of sparse high dimensional binary data

Śmieja

Hajto

Tabor

2019

Data Min Knowl Disc

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Clustering is one of the fundamental tools for preliminary analysis of data. While most of the clustering methods are designed for continuous data, sparse high-dimensional binary representations became very popular in various domains such as text mining or cheminformatics. The application of classical clustering tools to this type of data usually proves to be very inefficient, both in terms of computational complexity as well as in terms of the utility of the results. In this paper we propose a mixture model, SparseMix, for clustering of sparse high dimensional binary data, which connects model-based with centroid-based clustering. Every group is described by a representative and a probability distribution modeling dispersion from this representative. In contrast to classical mixture models based on the EM algorithm, SparseMix: is specially designed for the processing of sparse data; can be efficiently realized by an on-line Hartigan optimization algorithm; describes every cluster by the most representative vector. We have performed extensive experimental studies on various types of data, which confirmed that SparseMix builds partitions with a higher compatibility with reference grouping than related methods. Moreover, constructed representatives often better reveal the internal structure of data.

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Section: Distance-based Clusteringmentioning

confidence: 99%

Efficient mixture model for clustering of sparse high dimensional binary data

Śmieja

Hajto

Tabor

2019

Data Min Knowl Disc

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“…AcaGMM works well in practice; however, it has major limitations. First of all, the AcaGMM cost function does not necessarily decrease with iterations, which causes problems with the stop condition, see [39]. Since the method uses orthogonal projections and arc lengths, it is very hard to use AcaGMM for more complicated curves in higherdimensional spaces.…”

Section: Related Workmentioning

confidence: 99%

“…In [39], authors have constructed the afCEC (active function cross-entropy clustering) algorithm, which allows the clustering of data on sub-manifolds of ℝ d . The motivation comes from the observation that it is often profitable to describe nonlinear data by a smaller number of components with more complicated curved shapes to obtain a better fit of the data, see Fig.…”

Section: Introductionmentioning

confidence: 99%

Online updating of active function cross-entropy clustering

Spurek

Byrski

Tabor

2018

Pattern Anal Applic

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Gaussian mixture models have many applications in density estimation and data clustering. However, the model does not adapt well to curved and strongly nonlinear data, since many Gaussian components are typically needed to appropriately fit the data that lie around the nonlinear manifold. To solve this problem, the active function cross-entropy clustering (afCEC) method was constructed. In this article, we present an online afCEC algorithm. Thanks to this modification, we obtain a method which is able to remove unnecessary clusters very fast and, consequently, we obtain lower computational complexity. Moreover, we obtain a better minimum (with a lower value of the cost function). The modification allows to process data streams.

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“…In this paper, we introduce a semi-supervised clustering method, CEC-IB, based on partition-level side information. CEC-IB combines Cross-Entropy Clustering (CEC) [39,40,42], a model-based clustering technique, with the Information Bottleneck (IB) method [11,43] to build the smallest model that preserves the side information and provides a good model of the data distribution. In other words, CEC-IB automatically determines the required number of clusters to trade between model complexity, model accuracy, and consistency with the side information.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised cross-entropy clustering with information bottleneck constraint

Śmieja

Geiger

2017

Information Sciences

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In this paper, we propose a semi-supervised clustering method, CEC-IB, that models data with a set of Gaussian distributions and that retrieves clusters based on a partial labeling provided by the user (partition-level side information). By combining the ideas from cross-entropy clustering (CEC) with those from the information bottleneck method (IB), our method trades between three conflicting goals: the accuracy with which the data set is modeled, the simplicity of the model, and the consistency of the clustering with side information. Experiments demonstrate that CEC-IB has a performance comparable to Gaussian mixture models (GMM) in a classical semi-supervised scenario, but is faster, more robust to noisy labels, automatically determines the optimal number of clusters, and performs well when not all classes are present in the side information. Moreover, in contrast to other semi-supervised models, it can be successfully applied in discovering natural subgroups if the partition-level side information is derived from the top levels of a hierarchical clustering.

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Active function Cross-Entropy Clustering

Cited by 13 publications

References 47 publications

Efficient mixture model for clustering of sparse high dimensional binary data

Efficient mixture model for clustering of sparse high dimensional binary data

Online updating of active function cross-entropy clustering

Semi-supervised cross-entropy clustering with information bottleneck constraint

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