General split gaussian Cross–Entropy clustering

Spurek, Przemysław

doi:10.1016/j.eswa.2016.10.025

Cited by 11 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Nevertheless, unnatural assumption of the orthogonality of principal components causes two negative effects: the optimization process is time consuming and the model with the restriction that the coordinates are orthogonal can not accommodate data as good as the general one. Therefore, in this article we use more flexible modelthe General Split Normal [68] distribution: Definition 4.2. A density of the multivariate General Split Normal distribution is given by…”

Section: Multidimensional Split Gaussian Distributionmentioning

confidence: 99%

ICA based on asymmetry

Spurek

Tabor

Rola

et al. 2017

Pattern Recognition

View full text Add to dashboard Cite

“…The mixture model-based clustering literature has focused on the development of mixture distributions with more flexible parametric components like split distributions [6,7], skew distributions [8][9][10] and some other non-elliptical approaches [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Uniform Cross-entropy Clustering

Brzeski¹,

Spurek²

2017

Self Cite

View full text Add to dashboard Cite

Abstract. Robust mixture models approaches, which use non-normal distributions have recently been upgraded to accommodate data with fixed bounds. In this article we propose a new method based on uniform distributions and CrossEntropy Clustering (CEC). We combine a simple density model with a clustering method which allows to treat groups separately and estimate parameters in each cluster individually. Consequently, we introduce an effective clustering algorithm which deals with non-normal data.

show abstract

General split gaussian Cross–Entropy clustering

Cited by 11 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

ICA based on asymmetry

Uniform Cross-entropy Clustering

Contact Info

Product

Resources

About